collaborators

7 papers

cs.CV2026

CrossPortrait: Cross-Species Portrait Animation via Adversarial Motion Alignment

Gezhong Pan, Zhe Li, Yunfan Tao +7

Cross-species portrait animation, which aims to animate a source portrait of species A using a driving video of species B, is particularly challenging due to substantial anatomical…

cs.CV2026

Enhancing Video Physical Consistency via Role-aware Joint Training and Modality-decoupled Denoising

Guangting Zheng, Haojing Chen, Hao Li +6

While modern video diffusion models excel in visual fidelity, maintaining long-range physical consistency remains a formidable challenge. Conventional pixel-reconstruction objectiv…

cs.CV2026

SWIFT: Prompt-Adaptive Memory for Efficient Interactive Long Video Generation

Shanwen Tan, Hao Li, Jingtao Zhang +4

Streaming long-video generation faces a central challenge in continuous semantic switching, requiring adaptive memory to preserve coherent visual evolution. Current approaches rely…

cs.HC2026

EvoDesign: Agentic Editable Diagram Creation via Design Expertise Evolution

Tianfu Wang, Leilei Ding, Ziyang Tao +13

High-fidelity diagram creation requires the complex orchestration of semantic topology, visual styling, and spatial layout, posing a significant challenge for automated systems. Ex…

cs.RO2026

FAVLA: A Force-Adaptive Fast-Slow VLA model for Contact-Rich Robotic Manipulation

Yao Li, Peiyuan Tang, Wuyang Zhang +7

Force/torque feedback can substantially improve Vision-Language-Action (VLA) models on contact-rich manipulation, but most existing approaches fuse all modalities at a single opera…

cs.CV2026

ConsisDrive: Identity-Preserving Driving World Models for Video Generation by Instance Mask

Zhuoran Yang, Yanyong Zhang

Autonomous driving relies on robust models trained on large-scale, high-quality multi-view driving videos. Although world models provide a cost-effective solution for generating re…

cs.CV2026

InstaDrive: Instance-Aware Driving World Models for Realistic and Consistent Video Generation

Zhuoran Yang, Xi Guo, Chenjing Ding +3

Autonomous driving relies on robust models trained on high-quality, large-scale multi-view driving videos. While world models offer a cost-effective solution for generating realist…