activity
20232026
most citedZeroGS: Training 3D Gaussian Splatting from Unposed Images

1 citations · 1 across the 12 of their papers we have counts for

collaborators

23 papers

cs.CV2026

Caption-once, Frames-on-Demand: Visual-Need Routing for Budget-Aware Agentic Long Video Understanding

Weitong Cai, Hang Zhang, Yukai Huang +6

Long-video understanding on edge devices must reason over hours of content under tight compute and bandwidth budgets. Subsampling visual tokens loses temporal structure, while text…

cs.CV2026

Video Generative Models as Geometry Learner

Haosen Yang, Jifei Song, Zhensong Zhang +2

Recent generative approaches to geometry estimation adapt pretrained image diffusion models and treat the task as image-conditioned generation. Leveraging off-the-shelf image diffu…

cs.CV2026

Each Judge Its Own Yardstick: Discovering Per-VLM Taxonomies for Physical Video Evaluation

Yu Cao, Ziquan Liu, Zhensong Zhang +3

Maintaining physical consistency in video generators and world models increasingly relies on vision-language models (VLMs) as automated judges that provide reward signals, ranking…

cs.CV2026

TriMotion: Modality-Agnostic Camera Control for Video Generation

Seunghyun Shin, Jifei Song, Wooseok Jeon +2

Camera motion control is essential for directing viewpoint changes in generative systems. However, existing methods typically condition the generation process on a single specific…

cs.CV2026

LiteVSR: Lightweight Adaptation of Frozen Diffusion Transformers for Video Super-Resolution

Yu Cao, Ziquan Liu, Zhensong Zhang +3

Adapting large-scale pre-trained video generators for Video Super-Resolution (VSR) in novel domains remains computationally prohibitive. Methods that reformulate generation as dire…

cs.CV2026

Diffusion-Based Makeup Transfer with Facial Region-Aware Makeup Features

Zheng Gao, Debin Meng, Yunqi Miao +4

Current diffusion-based makeup transfer methods commonly use the makeup information encoded by off-the-shelf foundation models (e.g., CLIP) as condition to preserve the makeup styl…