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- Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering1,011
This work introduces Scaffold-GS, which uses an-chor points to distribute local 3D Gaussians, and predicts their attributes on-the-fly based on viewing direction and distance within the view frustum, and effectively reduces redun-dant Gaussians while delivering high-quality rendering.
- Extract Free Dense Labels from CLIP856
The finding suggests that MaskCLIP can serve as a new reliable source of supervision for dense prediction tasks to achieve annotation-free segmentation in pixel-level dense prediction, specifically in semantic segmentation.
- Detecting Visual Relationships with Deep Relational Networks529
The proposed Deep Relational Network is a novel formulation designed specifically for exploiting the statistical dependencies between objects and their relationships and achieves substantial improvement over state-of-the-art on two large data sets.
- CameraCtrl: Enabling Camera Control for Text-to-Video Generation496
Experimental results demonstrate the effectiveness of CameraCtrl in achieving precise camera control with different video generation models, marking a step forward in the pursuit of dynamic and customized video storytelling from textual and camera pose inputs.
- Towards Diverse and Natural Image Descriptions via a Conditional GAN482
A new framework based on Conditional Generative Adversarial Networks (CGAN) is proposed, which jointly learns a generator to produce descriptions conditioned on images and an evaluator to assess how well a description fits the visual content.
- FineGym: A Hierarchical Video Dataset for Fine-Grained Action Understanding467
FineGym is a new dataset built on top of gymnasium videos that provides temporal annotations at both action and sub-action levels with a three-level semantic hierarchy and systematically investigates different methods on this dataset and obtains a number of interesting findings.
- Temporal Pyramid Network for Action Recognition466
A generic Temporal Pyramid Network (TPN) at the feature-level is proposed, which can be flexibly integrated into 2D or 3D backbone networks in a plug-and-play manner and shows consistent improvements over other challenging baselines on several action recognition datasets.
- Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation458
This work presents an effective way to exploit the image prior captured by a generative adversarial network (GAN) trained on large-scale natural images and allows the generator to be fine-tuned on-the-fly in a progressive manner regularized by feature distance obtained by the discriminator in GAN.
- 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.
- Generative Diffusion Prior for Unified Image Restoration and Enhancement385
This work proposes the Generative Diffusion Prior (GDP) to effectively model the posterior distributions in an unsupervised sampling manner, and systematically explores a protocol of conditional guidance, which is verified more practical than the commonly used guidance way.
- DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior353
DiffBIR decouples blind image restoration problem into two stages: degradation removal and information regeneration, and proposes IRControlNet, a region-adaptive restoration guidance that can modify the denoising process during inference without model re-training, allowing users to balance realness and fidelity through a tunable guidance scale.
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- BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering333
BungeeNeRF is introduced, a progressive neural radiance field that achieves level-of-detail rendering across drastically varied scales and its support for high-quality rendering in different levels of detail is demonstrated.
- AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views287
This work introduces AnySplat, a feed-forward network for novel-view synthesis from uncalibrated image collections that greatly reduces rendering latency compared to optimization-based neural fields, bringing real-time novel-view synthesis within reach for unconstrained capture settings.
- Self-Supervised Scene De-Occlusion194
This paper makes the first attempt to address the problem of scene de-occlusion through a novel and unified framework that recovers hidden scene structures without ordering and amodal annotations as supervisions, via Partial Completion Network (PCNet)-mask (M) and -content (C), that learn to recover fractions of object masks and contents, respectively, in a self-supervised manner.
- Learning Hierarchical Cross-Modal Association for Co-Speech Gesture Generation169
This work proposes a novel framework named Hierarchical Audio-to-Gesture (HA2G) for co-speech gesture generation, and develops a contrastive learning strategy based on audio-text alignment for better audio representations.
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- Unsupervised 3D Shape Completion through GAN Inversion150
The proposed ShapeInversion uses a GAN pre-trained on complete shapes by searching for a latent code that gives a complete shape that best reconstructs the given partial input, and is capable of incorporating the rich prior captured in a well-trained generative model.
- Accelerating Diffusion Models via Early Stop of the Diffusion Process142
This work proposes a principled acceleration strategy, referred to as Early-Stopped DDPM (ES-DDPM), which obtains promising acceleration effect and performance improvement over representative baseline methods and demonstrates several attractive properties, including being orthogonal to existing acceleration methods, as well as simultaneously enabling both global semantic and local pixel-level control in image generation.
- DNA-Rendering: A Diverse Neural Actor Repository for High-Fidelity Human-centric Rendering131
This work presents DNA-Rendering, a large-scale, high-fidelity repository of human performance data for neural actor rendering, and provides a large-scale and quantitative benchmark in full-scale, with multiple tasks to evaluate the existing progress of novel view synthesis, novel pose animation synthesis, and novel identity rendering methods.
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- Move Forward and Tell: A Progressive Generator of Video Descriptions121
This work proposes a new approach, which produces a descriptive paragraph by assembling temporally localized descriptions by selecting a sequence of distinctive clips and generating sentences thereon in a coherent manner given a video.
- LN3Diff++: Scalable L atent N eural Fields D iffusion for Speedy 3D Generation117
A novel framework, LN3Diff++, designed to bridge the gap and facilitate fast, high-quality, and versatile conditional 3D generation and surpasses existing 3D diffusion methods in inference speed, requiring no per-instance optimization.
- Scene-aware Generative Network for Human Motion Synthesis103
This paper proposes a new framework, which factorizes the distribution of human motions into a distribution of movement trajectories conditioned on scenes and that of body pose dynamics conditioned on both scenes and trajectories, and derives a GAN-based learning approach.
- Recursive Visual Sound Separation Using Minus-Plus Net95
This paper proposes a novel framework, referred to as MinusPlus Network (MP-Net), for the task of visual sound separation, which obtains state-of-the-art results on two large scale datasets, across mixtures with different types and numbers of sounds.
Publication data from OpenAlex; citation counts are the higher of OpenAlex and Semantic Scholar, last synced 2026-10-12. 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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