1 citations · 3 across the 21 of their papers we have counts for
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VicEdit: Learning to Edit Videos from Visual In-Context Examples
Yuji Wang, Teng Hu, Yuheng Chen +6
Despite progress in instruction-based video editing, unimodal textual instructions inherently struggle to convey fine-grained textures and complex dynamics. To bridge this perceptu…
PersonaShot: Benchmarking Person-Centric Narrative Continuity in Multi-Shot Video Generation
Yuji Wang, Yuheng Chen, Teng Hu +7
Video generation is rapidly evolving from single-shot clips to multi-shot narratives, where the human character serves as the core narrative anchor. However, existing benchmarks ma…
ROAD: Reciprocal-Objective Alignment of Discriminative Semantics for 3D Shape Generation
Xiao Luo, Mingyang Du, Xin Zhou +5
High-fidelity 3D generation predominantly relies on scaling model capacity and data, which incurs prohibitive computational costs. This paradigm typically requires learning geometr…
Cycle-World: Mitigating Error Accumulation in Long-term Video World Models via Reverse-Prediction Cycle Consistency
Zihan Su, Teng Hu, Jiangning Zhang +4
Autoregressive diffusion models have enabled high-quality video generation, yet their sequential nature inherently suffers from error accumulation. In long-horizon video synthesis,…
JAVEDIT: Joint Audio-Visual Instruction-Guided Video Editing with Agentic Data Curation
Yinan Chen, Chuming Lin, Zhennan Chen +12
While instruction-based video editing has seen significant progress, joint audio-visual editing remains constrained by the absence of dedicated datasets and benchmarks. To bridge t…
MetaWorld: Scaling Multi-Agent Video World Model from Single-view Video Data
Teng Hu, Mingchun Lu, Yating Wang +6
Video world models are a foundational generative technology for embodied AI and the Metaverse, yet existing approaches are inherently limited to a single agent observing from a sin…
Spatial-Temporal Decoupled Reference Conditioning for Identity-Preserving Text-to-Video Generation
Yuheng Chen, Teng Hu, Yuji Wang +3
Identity-preserving video generation (IPVG) aims to synthesize high-fidelity videos that follow text prompts while faithfully preserving a reference identity. Despite recent progre…
L2P: Unlocking Latent Potential for Pixel Generation
Zhennan Chen, Junwei Zhu, Xu Chen +7
Pixel diffusion models have recently regained attention for visual generation. However, training advanced pixel-space models from scratch demands prohibitive computational and data…
Evolution of Video Generative Foundations
Teng Hu, Jiangning Zhang, Hongrui Huang +7
The rapid advancement of Artificial Intelligence Generated Content (AIGC) has revolutionized video generation, enabling systems ranging from proprietary pioneers like OpenAI's Sora…
InstanceV: Instance-Level Video Generation
Yuheng Chen, Teng Hu, Jiangning Zhang +3
Recent advances in text-to-video diffusion models have enabled the generation of high-quality videos conditioned on textual descriptions. However, most existing text-to-video model…
One-to-All Animation: Alignment-Free Character Animation and Image Pose Transfer
Shijun Shi, Jing Xu, Zhihang Li +5
Recent advances in diffusion models have greatly improved pose-driven character animation. However, existing methods are limited to spatially aligned reference-pose pairs with matc…
UltraGen: High-Resolution Video Generation with Hierarchical Attention
Teng Hu, Jiangning Zhang, Zihan Su +1
Recent advances in video generation have made it possible to produce visually compelling videos, with wide-ranging applications in content creation, entertainment, and virtual real…
PolyVivid: Vivid Multi-Subject Video Generation with Cross-Modal Interaction and Enhancement
Teng Hu, Zhentao Yu, Zhengguang Zhou +4
Despite recent advances in video generation, existing models still lack fine-grained controllability, especially for multi-subject customization with consistent identity and intera…
Image Inversion: A Survey from GANs to Diffusion and Beyond
Yinan Chen, Jiangning Zhang, Yali Bi +6
Image inversion is a fundamental task in generative models, aiming to map images back to their latent representations to enable downstream applications such as editing, restoration…
Improving Autoregressive Visual Generation with Cluster-Oriented Token Prediction
Teng Hu, Jiangning Zhang, Ran Yi +5
Employing LLMs for visual generation has recently become a research focus. However, the existing methods primarily transfer the LLM architecture to visual generation but rarely inv…
Exploring Real&Synthetic Dataset and Linear Attention in Image Restoration
Yuzhen Du, Teng Hu, Jiangning Zhang +6
Image restoration (IR) aims to recover high-quality images from degraded inputs, with recent deep learning advancements significantly enhancing performance. However, existing metho…
Textual Decomposition Then Sub-motion-space Scattering for Open-Vocabulary Motion Generation
Ke Fan, Jiangning Zhang, Ran Yi +6
Text-to-motion generation is a crucial task in computer vision, which generates the target 3D motion by the given text. The existing annotated datasets are limited in scale, result…
SaRA: High-Efficient Diffusion Model Fine-tuning with Progressive Sparse Low-Rank Adaptation
Teng Hu, Jiangning Zhang, Ran Yi +3
In recent years, the development of diffusion models has led to significant progress in image and video generation tasks, with pre-trained models like the Stable Diffusion series p…
SuperSVG: Superpixel-based Scalable Vector Graphics Synthesis
Teng Hu, Ran Yi, Baihong Qian +3
SVG (Scalable Vector Graphics) is a widely used graphics format that possesses excellent scalability and editability. Image vectorization, which aims to convert raster images to SV…
ID-Sculpt: ID-aware 3D Head Generation from Single In-the-wild Portrait Image
Jinkun Hao, Junshu Tang, Jiangning Zhang +7
While recent works have achieved great success on image-to-3D object generation, high quality and fidelity 3D head generation from a single image remains a great challenge. Previou…