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Works134 from public data
- Exploring Visual Relationship for Image Captioning933
This paper introduces a new design to explore the connections between objects for image captioning under the umbrella of attention-based encoder-decoder framework that novelly integrates both semantic and spatial object relationships into image encoder.
- Contextual Transformer Networks for Visual Recognition749
A novel Transformer-style module, i.e., Contextual Transformer (CoT) block, for visual recognition that fully capitalizes on the contextual information among input keys to guide the learning of dynamic attention matrix and thus strengthens the capacity of visual representation.
- Boosting Image Captioning with Attributes732
This paper presents Long Short-Term Memory with Attributes (LSTM-A) - a novel architecture that integrates attributes into the successful Convolutional Neural Networks plus Recurrent Neural Networks (RNNs) image captioning framework, by training them in an end-to-end manner.
- X-Linear Attention Networks for Image Captioning683
A unified attention block --- X-Linear attention block, that fully employs bilinear pooling to selectively capitalize on visual information or perform multi-modal reasoning is introduced.
- Jointly Modeling Embedding and Translation to Bridge Video and Language563
A novel unified framework, named Long Short-Term Memory with visual-semantic Embedding (LSTM-E), which can simultaneously explore the learning of LSTM and visual- semantic embedding and outperforms several state-of-the-art techniques in predicting Subject-Verb-Object (SVO) triplets.
- Transferrable Prototypical Networks for Unsupervised Domain Adaptation398
This paper presents Transferrable Prototypical Networks (TPN), which is end-to-end trained by jointly minimizing the distance across the prototypes on three types of data and KL-divergence of score distributions output by each pair of the prototypes.
- Video Captioning with Transferred Semantic Attributes364
This paper presents Long Short-Term Memory with Transferred Semantic Attributes (LSTM-TSA), a novel deep architecture that incorporates the transferred semantic attributes learnt from images and videos into the CNN plus RNN framework, by training them in an end-to-end manner.
- Exploring Object Relation in Mean Teacher for Cross-Domain Detection355
This work presents Mean Teacher with Object Relations (MTOR) that novelly remolds Mean Teacher under the backbone of Faster R-CNN by integrating the object relations into the measure of consistency cost between teacher and student modules.
- Memory Matching Networks for One-Shot Image Recognition309
Memory Matching Networks (MM-Net) is presented - a novel deep architecture that explores the training procedure, following the philosophy that training and test conditions must match, and improves one-shot accuracy on Omniglot and miniImageNet.
- Relation Distillation Networks for Video Object Detection238
Relation Distillation Networks is presented --- a new architecture that novelly aggregates and propagates object relation to augment object features for detection and achieves superior results when comparing to state-of-the-art methods.
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- Hierarchy Parsing for Image Captioning206
A new design to model a hierarchy from instance level, region level and region level to the whole image to delve into a thorough image understanding for captioning and is appealing in view that it is pluggable to any neural captioning models.
- Jointly Localizing and Describing Events for Dense Video Captioning199
This paper presents a novel framework for dense video captioning that unifies the localization of temporal event proposals and sentence generation of each proposal, by jointly training them in an end-to-end manner.
- To Create What You Tell180
A novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions.
- Dual Vision Transformer174
This paper proposes a novel Transformer architecture that elegantly exploits the global semantics for self-attention learning, namely Dual Vision Transformer (Dual-ViT).
- Incorporating Copying Mechanism in Image Captioning for Learning Novel Objects155
A new architecture that incorporates copying into the Convolutional Neural Networks plus Recurrent Neural Networks (RNN) image captioning framework, for describing novel objects in captions, and superior results are reported when compared to state-of-the-art deep models.
- HiDream-I1: A High-Efficient Image Generative Foundation Model with Sparse Diffusion Transformer152
HiDream-I1 is introduced, a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds and evolves to form a comprehensive image agent (HiDream-A1) capable of fully interactive image creation and refinement.
- Semantic-Conditional Diffusion Networks for Image Captioning*140
The deeply rooted conventions in learning Transformer-based encoder-decoder are broken, and a new diffusion model based paradigm tailored for image captioning, namely Semantic-Conditional Diffusion Networks (SCD-Net) is proposed.
- Comprehending and Ordering Semantics for Image Captioning128
A new recipe of Transformer-style structure is proposed, namely Comprehending and Ordering Semantics Networks (COS-Net), that novelly unifies an enriched semantic comprehending and a learnable semantic ordering processes into a single architecture.
- SeCo: Exploring Sequence Supervision for Unsupervised Representation Learning126
This paper compositions a trilogy of exploring the basic and generic supervision in the sequence from spatial, spatiotemporal and sequential perspectives, and uniquely regard the signals as the foundation in contrastive learning and derive a particular form named Sequence Contrastive Learning (SeCo).
- Temporal Deformable Convolutional Encoder-Decoder Networks for Video Captioning109
A novel design --- Temporal Deformable Convolutional Encoder-Decoder Networks (dubbed as TDConvED) that fully employ convolutions in both encoder and decoder networks for video captioning and increases CIDEr-D performance from 58.8% to 67.2% on MSVD.
- Exploring Structure-aware Transformer over Interaction Proposals for Human-Object Interaction Detection104
This work designs a novel Transformer-style HOI detector, i.e., Structure-aware Transformer over Interaction Proposals (STIP), for HOI detection that upgrades vanilla Transformer by encoding additionally the holistically semantic structure among interaction proposals as well as the locally spatial structure of human/object within each interaction proposal, so as to strengthen HOI predictions.
- Mocycle-GAN101
A new Motion-guided Cycle GAN, dubbed as Mocycle-GAN, that novelly integrates motion estimation into unpaired video translator that capitalizes on three types of constrains: adversarial constraint discriminating between synthetic and real frame, cycle consistency encouraging an inverse translation on both frame and motion, and motion translation validating the transfer of motion between consecutive frames.
- ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion88
This paper proposes a novel Object-centric Fusion (ObjectFusion) paradigm, which completely gets rid of camera-to-BEV transformation during fusion to align object-centric features across different modalities for 3D object detection.
- Deep Semantic Hashing with Generative Adversarial Networks88
This paper studies the exploration of generating synthetic data through semi-supervised generative adversarial networks (GANs), which leverages largely unlabeled and limited labeled training data to produce highly compelling data with intrinsic invariance and global coherence, for better understanding statistical structures of natural data.
- Exploring Category-Agnostic Clusters for Open-Set Domain Adaptation85
This paper addresses the problem of domain adaptation from closed-set to open-set situation by augmenting the state-of-the-art domain adaptation technique, Self-Ensembling, with category-agnostic clusters in target domain with SE-CC.
- Control3D: Towards Controllable Text-to-3D Generation84
This work presents the first attempt for text-to-3D generation conditioning on the additional hand-drawn sketch, namely Control3D, which enhances controllability for users and can generate accurate and faithful 3D scenes that align closely with the input text prompts and sketches.
- Click-through-based cross-view learning for image search84
This paper proposes a novel cross-view learning method for image search, named Click-through-based Cross-view Learning (CCL), by jointly minimizing the distance between the mappings of query and image in the latent subspace and preserving the inherent structure in each original space.
- Boosting Diffusion Models with Moving Average Sampling in Frequency Domain78
This paper reinterpret the iterative denoising process as model optimization and leverage a moving average mechanism to ensemble all the prior samples and lead to superior performances compared to the baselines, with almost negligible additional complexity cost.
- Pointing Novel Objects in Image Captioning76
This paper presents Long Short-Term Memory with Pointing (LSTM-P) --- a new architecture that facilitates vocabulary expansion and produces novel objects via pointing mechanism by augmenting standard deep captioning architectures with object learners.
- Stand-Alone Inter-Frame Attention in Video Models74
This paper presents a new recipe of inter-frame attention block, namely Stand-alone Inter-Frame Attention (SIFA), that novelly delves into the deformation across frames to estimate local self-attention on each spatial location.
- daBNN74
The proposed daBNN is a super fast inference framework that implements BNNs on ARM devices with several speed-up and memory refinement strategies for bit-packing, binarized convolution, and memory layout to enhance inference efficiency.
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- ControlStyle: Text-Driven Stylized Image Generation Using Diffusion Priors68
This paper presents a new diffusion model (ControlStyle) via upgrading a pre-trained text-to-image model with a trainable modulation network enabling more conditions of text prompts and style images, and introduces diffusion style and content regularizations to facilitate the learning of this modulation network.
- SD-DiT: Unleashing the Power of Self-Supervised Discrimination in Diffusion Transformer *61
This work novelly unleashes the self-supervised discrimination knowledge to boost DiT training by encoding discriminative pairs with student and teacher DiT encoders, designed to encourage the inter-image alignment in the selfsupervised embedding space.
- Auto-captions on GIF61
This work presents Auto-captions on GIF (ACTION), which is a new large-scale pre-training dataset for generic video understanding, and provides an evaluation of a Transformer-based encoder-decoder structure for vision-language pre- training, which is further adapted to video captioning downstream task and yields the compelling generalizability on MSR-VTT.
- Scheduled Sampling in Vision-Language Pretraining with Decoupled Encoder-Decoder Network61
This paper proposes a primary scheduled sampling strategy that elegantly mitigates such discrepancy via pretraining encoder-decoder in a two-pass manner and demonstrates the compelling generalizability of the pretrained encoding structure by fine-tuning on four VL understanding and generation downstream tasks.
- Single Shot Video Object Detector60
This paper presents Single Shot Video Object Detector (SSVD) — a new architecture that novelly integrates feature aggregation into a one-stage detector for object detection in videos and takes Feature Pyramid Network (FPN) as backbone network to produce multi-scale features.
- HIRI-ViT: Scaling Vision Transformer With High Resolution Inputs51
HIRI-ViT is built upon the seminal idea of decomposing the typical CNN operations into two parallel CNN branches in a cost-efficient manner and achieves to-date the best published Top-1 accuracy of 84.3% on ImageNet with 448×448 inputs.
- Retrieval Augmented Convolutional Encoder-decoder Networks for Video Captioning50
This article uniquely introduces a Retrieval Augmentation Mechanism (RAM) that enables the explicit reference to existing video-sentence pairs within any encoder-decoder captioning model, and presents Retrival Augmented Convolutional Encoder-Decoder Network (R-ConvED), which novelly integrates RAM into convolutional encoding structure to boost video captioning.
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- A Style and Semantic Memory Mechanism for Domain Generalization49
It is verified that it is critical for the network to be informative on what domain features are invariant and shared among in-stances, so that the network sharpens its understanding and improves its semantic discriminative ability.
- CoCo-BERT46
It is demonstrated the superiority of CoCo-BERT as a pre-trained structure over a wide range of downstream tasks (e.g., cross-modal retrieval, video question answering, and video captioning), and the CoCo proxy objective can be further integrated into any BERT-type encoder-decoder structure for video-language pre-training.
- TRIP: Temporal Residual Learning with Image Noise Prior for Image-to-Video Diffusion Models44
TRIP is a new recipe of image-to-video diffusion paradigm that pivots on image noise prior derived from static image to jointly trigger inter-frame relational reasoning and ease the coherent temporal modeling via temporal residual learning.
- Learning a Unified Sample Weighting Network for Object Detection44
A general loss function is devised to cover most region-based object detectors with various sampling strategies, and then a unified sample weighting network is proposed to predict a sample's task weights to consistently boost the detection accuracy.
- Hi3D: Pursuing High-Resolution Image-to-3D Generation with Video Diffusion Models42
High-resolution Image-to-3D model (Hi3D) is presented, a new video diffusion based paradigm that redefines a single image to multi-view images as 3D-aware sequential image generation (i.e., orbital video generation).
- 3D Cascade RCNN: High Quality Object Detection in Point Clouds42
This work presents a simple yet effective cascade architecture that allocates multiple detectors based on the voxelized point clouds in a cascade paradigm, pursuing higher quality 3D object detector progressively, and quantitatively defines the sparsity level of the points within 3D bounding box of each object as the point completeness score.
- MotionPro: A Precise Motion Controller for Image-to-Video Generation*40
This work presents MotionPro, a precise motion controller that novelly leverages region-wise trajectory and motion mask to regulate fine-grained motion synthesis and identify target motion category (i.e., object or camera moving), respectively.
- Modality-Agnostic Debiasing for Single Domain Generalization39
The superiority of the Modality-Agnostic Debiasing framework for single-DG scenarios with different modalities is validated, including recognition on 1D texts, 2D images, 3D point clouds, and semantic segmentation on2D images.
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- Convolutional Auto-encoding of Sentence Topics for Image Paragraph Generation38
A new design --- Convolutional Auto-Encoding (CAE) that purely employs convolutional and deconvolutional auto-encoding framework for topic modeling on the region-level features of an image and an architecture, namely CAE plus Long Short-Term Memory (dubbed as CAE-LSTM), that novelly integrates the learnt topics in support of paragraph generation.
- X-modaler37
This work proposes X-modaler --- a versatile and high-performance codebase that encapsulates the state-of-the-art cross-modal analytics into several general-purpose stages, and can be simply extended to power startup prototypes for other tasks in cross-Modal analytics, including visual question answering, visual commonsense reasoning, and cross- modal retrieval.
- Semi-supervised Hashing with Semantic Confidence for Large Scale Visual Search36
This paper proposes a novel semi-supervised hashing framework by leveraging semantic confidence, where a confidence factor is first assigned to each example by neighbor voting and click count in the scenarios with label and click-through data, and incorporated into the pairwise and triplet relationship learning for hashing.
- Representing Videos as Discriminative Sub-graphs for Action Recognition34
A new design of sub-graphs to represent and encode the discriminative patterns of each action in the videos of Something-Something V1 & V2 and Kinetics-400 datasets are introduced, and superior results are reported when comparing to state-of-the-art methods.
- Boosting Scene Graph Generation with Visual Relation Saliency33
This work presents a new design, named as Saliency-guided Message Passing (SMP), that boosts the generation of such scene graph structure with the guidance from the visual relation saliency, and empirically validate the compelling generalizability of the learned scene graphs via SMP on downstream tasks like cross-model retrieval and image captioning.
- Dynamic Temporal Filtering in Video Models32
This paper presents a new recipe of temporal feature learning, namely Dynamic Temporal Filter (DTF), that novelly performs spatial-aware temporal modeling in frequency domain with large temporal receptive field and dynamically learns a specialized frequency filter for every spatial location to model its long-range temporal dynamics.
- Transferrable Contrastive Learning for Visual Domain Adaptation30
This work presents a particular paradigm of self-supervised learning tailored for domain adaptation, i.e., Transferrable Contrastive Learning (TCL), which links the SSL and the desired cross-domain transferability congruently, and finds contrastive learning intrinsically a suitable candidate fordomain adaptation.
- 3DStyle-Diffusion: Pursuing Fine-grained Text-driven 3D Stylization with 2D Diffusion Models27
This work proposes a new 3DStyle-Diffusion model that triggers fine-grained stylization of 3D meshes with additional controllable appearance and geometric guidance from 2D Diffusion models, and elegantly integrates both image rendering via implicit MLP networks and diffusion process of image synthesis in an end-to-end fashion.
- Uni-EDEN: Universal Encoder-Decoder Network by Multi-Granular Vision-Language Pre-training27
A pre-trainable Universal Encoder-DEcoder Network (Uni-EDEN), consisting of three modules: object and sentence encoders that separately learns the representations of each modality and sentence decoder that enables both multi- modal reasoning and sentence generation via inter-modal interaction.
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- Smart Director: An Event-Driven Directing System for Live Broadcasting25
The Smart Director system is the first end-to-end automated directing system for multi-camera sports broadcasting, completely driven by the semantic understanding of sports events, and is also the first system to solve the novel problem of multi-view joint event detection by cross-view relation modeling.
- HiDream-I1: An Open-Source High-Efficient Image Generative Foundation Model24
HiDream-I1, a new open-source image generative foundation model with 17B parameters that achieves state-of-the-art image generation quality within seconds, and HiDream-E1, a new image editing model namely HiDream-E1 that quickly gained strong traction in the community.
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- Visual Autoregressive Modeling for Instruction-Guided Image Editing22
This paper presents VAREdit, a visual autoregressive (VAR) framework that reframes image editing as a next-scale prediction problem and introduces a Scale-Aligned Reference (SAR) module, which injects scale-matched conditioning information into the first self-attention layer.
- A Low Rank Promoting Prior for Unsupervised Contrastive Learning22
A novel probabilistic graphical model that effectively incorporates the low rank promoting prior into the framework of contrastive learning, referred to as LORAC, which clearly surpasses the state-of-the-art approaches on multiple benchmarks, including image classification, object detection, instance segmentation and keypoint detection.
- Transforming Radiance Field with Lipschitz Network for Photorealistic 3D Scene Stylization21
A concise and flexible learning framework namely LipRF is built, which upgrades arbitrary 2D PST methods with Lipschitz mapping tailored for the 3D scene, and devise an adaptive regularization to balance the reconstruction and stylization.
- Improving Text-Guided Object Inpainting with Semantic Pre-inpainting20
This paper proposes to decompose the typical single-stage object inpainting into two cascaded processes: 1) semantic pre-inpainting that infers the semantic features of desired objects in a multi-modal feature space; 2) high-fieldity object generation in diffusion latent space that pivots on such inpainted semantic features.
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- Click-through-based Subspace Learning for Image Search18
It is demonstrated that the above two fundamental challenges can be mitigated by jointly exploring the subspace learning and the use of click-through data and the former aims to create a latent subspace with the ability in comparing information from the original incomparable views.
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- A Roadmap for Big Model17
This paper discusses not only the BM technologies themselves but also the prerequisites for BM training and applications with BMs, dividing the BM review into four parts: Resource, Models, Key Technologies and Application.
- Deep Metric Learning With Density Adaptivity17
This paper integrates density, i.e., the measure of data concentration in the representation, into the optimization of DML frameworks to adaptively balance inter-class similarity and intra-class variation by training the architecture in an end-to-end manner.
- Image search by graph-based label propagation with image representation from DNN16
This paper proposes a graph-based label propagation algorithm that employs neighborhood graph search to find the nearest neighbors on an image similarity graph built up with visual representations from deep neural networks and further aggregates their clicked queries/click counts to get the labels of the new image.
- Bottom-up and Top-down Object Inference Networks for Image Captioning15
This work presents the Bottom-up and Top-down Object inference Network (BTO-Net), which novelly exploits the object sequence of interest as top-down signals to guide image captioning and obtains competitive performances on the COCO benchmark.
- Pursuing Temporal-Consistent Video Virtual Try-On via Dynamic Pose Interaction13
A new framework, namely Dynamic Pose Interaction Diffusion Models (DPIDM), to leverage diffusion models to delve into dynamic pose interactions for video virtual try-on and capitalizes on a temporal regularized attention loss between consecutive frames to enhance temporal consistency.
- Incorporating Visual Correspondence into Diffusion Model for Virtual Try-On13
This work proposes to explicitly capitalize on visual correspondence as the prior to tame diffusion process instead of simply feeding the whole garment into UNet as the appearance reference, and interprets the fine-grained appearance and texture details as a set of structured semantic points, which are augmented into 3D-aware cues with depth/normal map of target person.
- Unleashing Text-to-Image Diffusion Prior for Zero-Shot Image Captioning13
A novel Patch-wise Cross-modal feature Mix-up (PCM) mechanism to adaptively mitigate the unfaithful contents in a fine-grained manner during training, which can be integrated into most of encoder-decoder frameworks, and ranks first in both in-domain and cross-domain zero-shot image captioning.
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- Contextual and selective attention networks for image captioning12
A contextual and selective attention network that novelly memorizes contextual attention and brings out the principal components from each attention is presented, and extensive experiments on the COCO image captioning dataset demonstrate the superiority of CoSA-Net.
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- Boosting Vision-and-Language Navigation with Direction Guiding and Backtracing10
A Direction-guided Navigator Agent (DNA) that novelly integrates direction clues derived from instructions into the essential encoder-decoder navigation framework and strengthens the alignment between instruction and action sequence.
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- Seeing Bot10
A video captioning bot, named Seeing Bot, which can generate a natural language description about what it is seeing in near real time and sends the speech to an end user via an earphone.
- SPE-Net: Boosting Point Cloud Analysis via Rotation Robustness Enhancement9
Intriguingly, it is revealed that by locally encoding the rotation information through SPE-Net, the rotation-invariant features are still of critical importance in benefiting the test samples without any actual global rotation.
- Learning Click-Based Deep Structure-Preserving Embeddings with Visual Attention9
A novel click-based Deep Structure-Preserving Embeddings with visual Attention (DSPEA) model is presented, which consists of two components: deep convolutional neural networks followed by image embedding layers for learning visual embedding, and a deep neural networks for generating query semantic embedding.
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- 3D Creation at Your Fingertips: From Text or Image to 3D Assets8
An automatic 3D creation system, which can create realistic 3D assets solely from a text or image prompt without requiring any specialized 3D modeling skills, and a coarse-to-fine framework to achieve this goal.
- Core-Text: Improving Scene Text Detection with Contrastive Relational Reasoning8
This work quantitatively analyze the sub-text problem and presents a simple yet effective design, COntrastive RElation (CORE) module, to mitigate that issue and integrates the CORE module into a two-stage text detector of Mask R-CNN and devise the text detector CORE-Text.
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- Animating Your Life8
An unified unpaired video-to-video translator is utilized to explore both appearance structure and temporal continuity in video synthesis, which can transform real-world video into cartoon or ink-wash animation in real-time.
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- VTON-VLLM: Aligning Virtual Try-On Models with Human Preferences5
A dataset of synthesized VTON images annotated with human judgments across multiple perceptual criteria is curated, and a vision large language model (VLLM), namely VTON-VLLM, is learnt on these annotations, which functions as a unified “fashion expert” and is capable of both evaluating and steering VTON synthesis towards human preferences.
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- Creatively Upscaling Images with Global-Regional Priors4
This work presents C-Upscale, a new recipe of tuning-free image upscaling that pivots on global-regional priors derived from given global prompt and estimated regional prompts via Multimodal LLM to generate ultra-high-resolution images with higher visual fidelity and more creative regional details.
- Identity-Preserving Video Generation Challenge4
The Identity-Preserving Video Generation (IPVG) grand challenge is introduced, a novel dataset comprising approximately 500,000 video-prompt pairs with 200,000 unique identities, each coupled with a reference identity image is released.
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- Silver-Bullet-3D at ManiSkill 2021: Learning-from-Demonstrations and Heuristic Rule-based Methods for Object Manipulation4
An overview and comparative analysis of the systems designed for the following two tracks in SAPIEN ManiSkill Challenge 2021: No Interaction Track and No Restriction Track are presented.
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- Out-of-Distribution Detection with Hilbert-Schmidt Independence Optimization3
This paper proposes an alternative probabilistic paradigm that is both practically useful and theoretically viable for the OOD detection tasks and imposes statistical independence between inlier and outlier data during training, in order to ensure that inlier data reveals little information about OOD data to the deep estimator during training.
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- iDirector: An Intelligent Directing System for Live Broadcast3
iDirector is demonstrated, which aims to mimic the human-in-loop live broadcasting process by aggregating the input multi-camera video streaming into the final output program video (PGM video) for audience.
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- Edit-by-Example: Adaptive Exemplar-Based Image Editing2
An exemplar-based image editing framework named Edit-by-Example, which eliminates the reliance on textual prompts, and requires only a single pair of before-and-after images to encapsulate the desired editing effect that can readily be applied on the user-provided query image without any model fine-tuning.
- Boosting Relationship Detection in Images with Multi-Granular Self-Supervised Learning2
This article integrates the standard relationship detection objective with a series of proposed self-supervised proxy tasks, which is named as Multi-Granular Self-Supervised learning (MGS), appealing in view that it is pluggable to any neural relationship detection models by simply including the proxy tasks during training, without increasing the computational cost at inference.
- CoCo-BERT: Improving Video-Language Pre-training with Contrastive Cross-modal Matching and Denoising2
- VireoJD-MM @ TRECVid 2019: Activities in Extended Video (ActEV).2
The system developed for Activities in Extended Video(ActEV) task at TRECVid 2019 and the achieved results are described and the system is extended for two aspects separately: better object detection and advanced two-stream action classification.
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- Talk, Imagine, Evolve: A Unified Multimodal Agent for Seamless Visual Generation and Editing1
This paper demonstrates a pioneering unified multimodal agent that transforms complex visual content creation into an intuitive, conversational experience, allowing users to talk, imagine, and evolve their ideas within a single AI interface.
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- Motion Controlled Text-to-4D Generation with Multi-View Video Diffusion Model–
Motion4D is proposed, a rigging-free framework that animates static humanoid meshes with arbitrary initial poses by replicating the motion pattern of a reference sequence by adopting a generate-then-animate paradigm and introducing a Gaussian-Proxied Mesh Animation algorithm.
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Publication data from OpenAlex, with missing venues and authors filled in from Crossref; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-16. One-sentence summaries under some papers are written by Semantic Scholar’s model. Citation counts may be lower than on Google Scholar, which indexes more sources.
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