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Works68 from public data
- VBench: Comprehensive Benchmark Suite for Video Generative Models2,062
VBench is a comprehensive benchmark suite that dissects “video generation quality” into specific, hierarchical, and disentangled dimensions, each with tailored prompts and evaluation methods, and investi-gate the gaps between video and image generation models.
- AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning1,729
The proposed AnimateDiff is a practical framework for animating personalized T2I models without requiring model-specific tuning and MotionLoRA is a lightweight fine-tuning technique for AnimateDiff that enables a pre-trained motion module to adapt to new motion patterns, such as different shot types, at a low training and data collection cost.
- InternVid: A Large-scale Video-Text Dataset for Multimodal Understanding and Generation557
A scalable approach to autonomously build a high-quality video-text dataset with large language models (LLM), thereby showcasing its efficacy in learning video-language representation at scale and introducing ViCLIP, a video- Text representation learning model based on ViT-L.
- LaVie: High-Quality Video Generation with Cascaded Latent Diffusion Models426
The incorporation of simple temporal self-attentions, coupled with rotary positional encoding, adequately captures the temporal correlations inherent in video data and proves that the process of joint image-video fine-tuning plays a pivotal role in producing high-quality and creative outcomes.
- SinSR: Diffusion-Based Image Super-Resolution in a Single Step378
This work first derive a deterministic sampling process from the most recent state-of-the-art (SOTA) method for accelerating diffusion-based SR, which can be distilled into a student model that performs SR within only one inference step, resulting in a remarkable up to × 10 speedup for inference.
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- SEINE: Short-to-Long Video Diffusion Model for Generative Transition and Prediction267
This paper proposes a random-mask video diffusion model to automatically generate transitions based on textual descriptions that generates transition videos that ensure coherence and visual quality and validate the effectiveness of the approach over existing methods for generative transition and prediction.
- Latent Image Animator: Learning to Animate Images via Latent Space Navigation234
The Latent Image Animator is introduced, a self-supervised autoencoder that evades need for structure representation and significantly outperforms state-of-art methods on VoxCeleb, Taichi and TED-talk datasets w.r.t. generated quality.
- Joint Generative and Contrastive Learning for Unsupervised Person Re-identification196
This paper incorporates a Generative Adversarial Network and a contrastive learning module into one joint training framework, and proposes a mesh-based view generator that significantly outperforms state-of-the-art methods under both, fully unsupervised and unsuper supervised domain adaptive settings on several large scale ReID dat-sets.
- ImaGINator: Conditional Spatio-Temporal GAN for Video Generation157
A novel conditional GAN architecture, namely ImaGINator, which given a single image, a condition (label of a facial expression or action) and noise, decomposes appearance and motion in both latent and high level feature spaces, generating realistic videos.
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- EpiDiff: Enhancing Multi-View Synthesis via Localized Epipolar-Constrained Diffusion90
The proposed EpiDiff is a localized interactive multiview diffusion model to insert a lightweight epipolar attention block into the frozen diffusion model, leveraging epipolar constraints to enable cross-view interaction among feature maps of neighboring views.
- Vlogger: Make Your Dream A Vlog85
Vlogger, a generic AI system for generating a minute-level video blog of user de-scriptions, smartly leverages Large Language Model (LLM) as Director and decomposes a long video generation task of vlog into four key stages, where various foundation models play the critical roles of vlog profession-als.
- UNIK: A Unified Framework for Real-world Skeleton-based Action Recognition66
Experimental results show that the proposed UNIK, with pre-training on Posetics, generalizes well and outperforms state-of-the-art when transferred onto four target action classification datasets: Toyota Smarthome, Penn Action, NTU-RGB+D 60 andNTU- RGB+D 120.
- Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models61
This work presents Vchitect-2.0, a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation, and proposes a Memory-efficient Training framework that incorporates hybrid parallelism and other memory reduction techniques, enabling efficient training of long video sequences on distributed systems.
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- LEO: Generative Latent Image Animator for Human Video Synthesis57
Extensive quantitative and qualitative analysis suggests that LEO significantly improves coherent synthesis of human videos over previous methods on the datasets TaichiHD, FaceForensics and CelebV-HQ, as well as content-preserving video editing.
- View-Invariant Skeleton Action Representation Learning via Motion Retargeting42
This work introduces ViA, a novel View-Invariant Autoencoder for self-supervised skeleton action representation learning that leverages motion retargeting between different human performers as a pretext task, in order to disentangle the latent action-specific ‘Motion’ features on top of the visual representation of a 2D or 3D skeleton sequence.
- LIA: Latent Image Animator41
The Latent Image Animator (LIA), streamlined to animate high-resolution images, outperforms state-of-the-art on VoxCeleb, TaichiHD, and TED-talk datasets with respect to video quality and spatio-temporal consistency.
- Learning Invariance From Generated Variance for Unsupervised Person Re-Identification41
This article proposes to replace traditional data augmentation with a generative adversarial network (GAN) that is targeted to generate augmented views for contrastive learning and achieves new state-of-the-art unsupervised person ReID performance on mainstream large-scale benchmarks.
- Ouroboros3D: Image-to-3D Generation via 3D-aware Recursive Diffusion36
Ouroboros3D, an end-to-end trainable framework that integrates multi-view generation and 3D reconstruction into a recursive diffusion process through feedback mechanism, achieves superior multi-view consistency and producing 3D models with higher geometric realism.
- Long-Term Rhythmic Video Soundtracker31
Long-Term Rhythmic Video Soundtracker (LORIS), a novel framework to synthesize long-term conditional waveforms consisting of a latent conditional diffusion probabilistic model to perform waveform synthesis, is presented.
- CineTrans: Learning to Generate Videos with Cinematic Transitions via Masked Diffusion Models29
This work introduces CineTrans, a novel framework for generating coherent multi-shot videos with cinematic, film-style transitions, and proposes specialized evaluation metrics for transition control, temporal consistency and overall quality.
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- Hierarchical Diffusion Autoencoders and Disentangled Image Manipulation26
The proposed Hierarchical Diffusion Autoencoders (HDAE) that exploits the coarse-to-fine feature hierarchy for the latent space of diffusion models and proposes a truncated feature based approach for disentangled image manipulation.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-15. 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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