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Works72 from public data
- 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.
- 1,226
- 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.
- Temporal Pyramid Network for Action Recognition456
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.
- 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.
- 386
- Seedance 1.0: Exploring the Boundaries of Video Generation Models276
Compared to state-of-the-art video generation models, Seedance 1.0 stands out with high-quality and fast video generation having superior spatiotemporal fluidity with structural stability, precise instruction adherence in complex multi-subject contexts, native multi-shot narrative coherence with consistent subject representation.
- Seedream 4.0: Toward Next-generation Multimodal Image Generation253
This work introduces Seedream 4.0, an efficient and high-performance multimodal image generation system that unifies text-to-image (T2I) synthesis, image editing, and multi-image composition within a single framework, and develops a highly efficient diffusion transformer with a powerful VAE which allows for efficient training of the model.
- Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis216
This work shows that highly-structured semantic hierarchy emerges in the deep generative representations from the state-of-the-art GANs like StyleGAN and BigGAN, trained for scene synthesis, and quantifies the causality between the layer-wise activations and the semantics occurring in the output image.
- Instance Localization for Self-supervised Detection Pretraining169
This paper proposes a new self-supervised pretext task, called instance localization, based on the inherent difference between classification and detection, and shows that integration of bounding boxes into pretraining promotes better task alignment and architecture alignment for transfer learning.
- Pose Guided Human Video Generation163
A pose guided method to synthesize human videos in a disentangled way: plausible motion prediction and coherent appearance generation and enforce semantic consistency between the generated and ground-truth poses at a high feature level.
- Diffusion Adversarial Post-Training for One-Step Video Generation153
This work proposes Adversarial Post-Training (APT) against real data following diffusion pre-training for one-step video generation, and introduces several improvements to the model architecture and training procedures, along with an approximated R1 regularization objective.
- 3D-aware Image Synthesis via Learning Structural and Textural Representations145
This work proposes a novel framework, termed as VolumeGAN, for high-fidelity 3D-aware image synthesis, through explicitly learning a structural representation and a textural representation in a Generative Adversarial Network.
- 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.
- 130
- Generative Hierarchical Features from Synthesizing Images126
This work considers the pre-trained StyleGAN generator as a learned loss function and utilizes its layer-wise representation to train a novel hierarchical encoder, termed as Generative Hierarchical Feature (GH-Feat), which has strong transferability to both generative and discriminative tasks.
- Long Context Tuning for Video Generation119
This work introduces Long Context Tuning (LCT), a training paradigm that expands the context window of pre-trained single-shot video diffusion models to learn scene-level consistency directly from data, enabling both joint and auto-regressive shot generation without additional parameters.
- CameraCtrl: Enabling Camera Control for Video Diffusion Models107
This paper builds on improved single-clip capabilities of CameraCtrl II by allowing users to iteratively specify camera trajectories for generating coherent video sequences, and enables extended scene exploration by allowing users to iteratively specify camera trajectories for generating coherent video sequences.
- CameraCtrl II: Dynamic Scene Exploration via Camera-Controlled Video Diffusion Models107
This paper builds on improved single-clip capabilities of CameraCtrl II by allowing users to iteratively specify camera trajectories for generating coherent video sequences, and enables extended scene exploration by allowing users to iteratively specify camera trajectories for generating coherent video sequences.
- Penalizing Top Performers: Conservative Loss for Semantic Segmentation Adaptation107
This work proposes a novel loss function, i.e., Conservative Loss, which penalizes the extreme good and bad cases while encouraging the moderate examples and enables the network to learn features that are discriminative by gradient descent and are invariant to the change of domains via gradient ascend method.
- Mixture of Contexts for Long Video Generation94
This work recast long-context video generation as an internal information retrieval task and proposes a simple, learnable sparse attention routing module, Mixture of Contexts (MoC), as an effective long-term memory retrieval engine.
- 92
- Seaweed-7B: Cost-Effective Training of Video Generation Foundation Model90
This technical report presents a cost-efficient strategy for training a video generation foundation model called Seaweed-7B trained from scratch using 665,000 H100 GPU hours and highlights the key design decisions that enhance the performance of the medium-sized diffusion model.
- DisCoScene: Spatially Disentangled Generative Radiance Fields for Controllable 3D-aware Scene Synthesis83
The proposed model spatially disentangles the whole scene into object-centric generative radiance fields by learning on only 2D images with the global-local discrimination, and obtains the generation fidelity and editing flexibility of individual objects while being able to efficiently compose objects and the background into a complete scene.
- SeedVR: Seeding Infinity in Diffusion Transformer Towards Generic Video Restoration75
SeedVR is a diffusion transformer designed to handle real-world video restoration with arbitrary length and resolution, and achieves highly-competitive performance on both synthetic and real-world benchmarks, as well as AI-generated videos.
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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