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20242026
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cs.CV2026

Co-generation of Layout and Shape from Text via Autoregressive 3D Diffusion

Zhenggang Tang, Yuehao Wang, Yuchen Fan +9

Recent text-to-scene generation approaches largely reduced the manual efforts required to create 3D scenes. However, their focus is either to generate a scene layout or to generate…

cs.CV2025

Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields

Shijie Zhou, Hui Ren, Yijia Weng +8

Recent advancements in 2D and multimodal models have achieved remarkable success by leveraging large-scale training on extensive datasets. However, extending these achievements to…

cs.CV2024

4DGen: Grounded 4D Content Generation with Spatial-temporal Consistency

Yuyang Yin, Dejia Xu, Zhangyang Wang +2

Aided by text-to-image and text-to-video diffusion models, existing 4D content creation pipelines utilize score distillation sampling to optimize the entire dynamic 3D scene. Howev…

cs.CV2024

Cavia: Camera-controllable Multi-view Video Diffusion with View-Integrated Attention

Dejia Xu, Yifan Jiang, Chen Huang +5

In recent years there have been remarkable breakthroughs in image-to-video generation. However, the 3D consistency and camera controllability of generated frames have remained unso…

cs.CV2024

CamCo: Camera-Controllable 3D-Consistent Image-to-Video Generation

Dejia Xu, Weili Nie, Chao Liu +4

Recently video diffusion models have emerged as expressive generative tools for high-quality video content creation readily available to general users. However, these models often…

cs.CV2024

Diffusion4D: Fast Spatial-temporal Consistent 4D Generation via Video Diffusion Models

Hanwen Liang, Yuyang Yin, Dejia Xu +5

The availability of large-scale multimodal datasets and advancements in diffusion models have significantly accelerated progress in 4D content generation. Most prior approaches rel…