15 citations · 27 across the 17 of their papers we have counts for
11 papers · 1 filter
HarnessEval-W: Agentifying the Evaluation of Visual Worlds
Weiliang Chen, Haowen Sun, Jun Gao +40
A benchmark should deliver more than a scalar score: what makes an evaluation trustworthy is the reasoning that justifies the score. This is especially critical for world models, w…
Code as Worlds: Agentic Discovery of Executable World Representations for Physical Reasoning
Hanyang Wang, Yimo Cai, Weiliang Chen +14
Physical understanding and reasoning depend on forming compact and generalizable representations of the world. While modern vision-language models can recognize and explain diverse…
MBench: A Comprehensive Benchmark on Memory Capability for Video World Models
Shengjun Zhang, Zhang Zhang, Simin Huang +11
Recent advancements in video-based world models have demonstrated an unprecedented ability to synthesize high-fidelity visual sequences. However, a fundamental gap persists between…
CFG-Ctrl: Control-Based Classifier-Free Diffusion Guidance
Hanyang Wang, Yiyang Liu, Jiawei Chi +3
Classifier-Free Guidance (CFG) has emerged as a central approach for enhancing semantic alignment in flow-based diffusion models. In this paper, we explore a unified framework call…
LangScene-X: Reconstruct Generalizable 3D Language-Embedded Scenes with TriMap Video Diffusion
Fangfu Liu, Hao Li, Jiawei Chi +4
Recovering 3D structures with open-vocabulary scene understanding from 2D images is a fundamental but daunting task. Recent developments have achieved this by performing per-scene…
VideoScene: Distilling Video Diffusion Model to Generate 3D Scenes in One Step
Hanyang Wang, Fangfu Liu, Jiawei Chi +1
Recovering 3D scenes from sparse views is a challenging task due to its inherent ill-posed problem. Conventional methods have developed specialized solutions (e.g., geometry regula…
Video-T1: Test-Time Scaling for Video Generation
Fangfu Liu, Hanyang Wang, Yimo Cai +3
With the scale capability of increasing training data, model size, and computational cost, video generation has achieved impressive results in digital creation, enabling users to e…
ReconX: Reconstruct Any Scene from Sparse Views with Video Diffusion Model
Fangfu Liu, Wenqiang Sun, Hanyang Wang +5
Advancements in 3D scene reconstruction have transformed 2D images from the real world into 3D models, producing realistic 3D results from hundreds of input photos. Despite great s…
Physics3D: Learning Physical Properties of 3D Gaussians via Video Diffusion
Fangfu Liu, Hanyang Wang, Shunyu Yao +3
In recent years, there has been rapid development in 3D generation models, opening up new possibilities for applications such as simulating the dynamic movements of 3D objects and…
Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image
Kailu Wu, Fangfu Liu, Zhihan Cai +5
In this work, we introduce Unique3D, a novel image-to-3D framework for efficiently generating high-quality 3D meshes from single-view images, featuring state-of-the-art generation…
Make-Your-3D: Fast and Consistent Subject-Driven 3D Content Generation
Fangfu Liu, Hanyang Wang, Weiliang Chen +2
Recent years have witnessed the strong power of 3D generation models, which offer a new level of creative flexibility by allowing users to guide the 3D content generation process t…