5 citations · 13 across the 22 of their papers we have counts for
24 papers
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…
Open Evaluation Agent: Efficient and Promptable Evaluation of Visual Generative Models
Shulin Tian, Ziqi Huang, Fan Zhang +3
Recent advances in visual generative models have enabled high-quality image and video generation, but evaluating these models often demands sampling hundreds or thousands of images…
Apple-: Benchmarking Thinking with Video Towards Law-Grounded Physical Intelligence
Runmao Yao, Kairui Hu, Yukang Cao +11
Modern video generation models are increasingly hailed as emerging world models with an internalized grasp of physical law. Yet existing benchmarks largely evaluate physical plausi…
Is Your Driving World Model an All-Around Player?
Lingdong Kong, Ao Liang, Tianyi Yan +20
Today's driving world models can generate remarkably realistic dash-cam videos, yet no single model excels universally. Some generate photorealistic textures but violate basic phys…
AnimationBench: Are Video Models Good at Character-Centric Animation?
Leyi Wu, Pengjun Fang, Kai Sun +8
Video generation has advanced rapidly, with recent methods producing increasingly convincing animated results. However, existing benchmarks-largely designed for realistic videos-st…
Prompt Relay: Inference-Time Temporal Control for Multi-Event Video Generation
Gordon Chen, Ziqi Huang, Ziwei Liu
Video diffusion models have achieved remarkable progress in generating high-quality videos. However, these models struggle to represent the temporal succession of multiple events i…
Agentic World Modeling: Foundations, Capabilities, Laws, and Beyond
Meng Chu, Xuan Billy Zhang, Kevin Qinghong Lin +47
As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment dynamics becomes a central bottleneck. Agents that ma…
Demystifying Video Reasoning
Ruisi Wang, Zhongang Cai, Fanyi Pu +11
Recent advances in video generation have revealed an unexpected phenomenon: diffusion-based video models exhibit non-trivial reasoning capabilities. Prior work attributes this to a…
WorldLens: Full-Spectrum Evaluations of Driving World Models in Real World
Ao Liang, Lingdong Kong, Tianyi Yan +19
Generative world models are reshaping embodied AI, enabling agents to synthesize realistic 4D driving environments that look convincing but often fail physically or behaviorally. D…
RealDPO: Real or Not Real, that is the Preference
Guo Cheng, Danni Yang, Ziqi Huang +3
Video generative models have recently achieved notable advancements in synthesis quality. However, generating complex motions remains a critical challenge, as existing models often…
Simulating the Visual World with Artificial Intelligence: A Roadmap
Jingtong Yue, Ziqi Huang, Zhaoxi Chen +3
The landscape of video generation is shifting, from a focus on generating visually appealing clips to building virtual environments that support interaction and maintain physical p…
The Quest for Generalizable Motion Generation: Data, Model, and Evaluation
Jing Lin, Ruisi Wang, Junzhe Lu +10
Despite recent advances in 3D human motion generation (MoGen) on standard benchmarks, existing text-to-motion models still face a fundamental bottleneck in their generalization cap…
Uni-MMMU: A Massive Multi-discipline Multimodal Unified Benchmark
Kai Zou, Ziqi Huang, Yuhao Dong +7
Unified multimodal models aim to jointly enable visual understanding and generation, yet current benchmarks rarely examine their true integration. Existing evaluations either treat…
Stencil: Subject-Driven Generation with Context Guidance
Gordon Chen, Ziqi Huang, Cheston Tan +1
Recent text-to-image diffusion models can generate striking visuals from text prompts, but they often fail to maintain subject consistency across generations and contexts. One majo…
Cut2Next: Generating Next Shot via In-Context Tuning
Jingwen He, Hongbo Liu, Jiajun Li +4
Effective multi-shot generation demands purposeful, film-like transitions and strict cinematic continuity. Current methods, however, often prioritize basic visual consistency, negl…
ShotBench: Expert-Level Cinematic Understanding in Vision-Language Models
Hongbo Liu, Jingwen He, Yi Jin +11
Cinematography, the fundamental visual language of film, is essential for conveying narrative, emotion, and aesthetic quality. While recent Vision-Language Models (VLMs) demonstrat…
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
Dian Zheng, Ziqi Huang, Hongbo Liu +9
Video generation has advanced significantly, evolving from producing unrealistic outputs to generating videos that appear visually convincing and temporally coherent. To evaluate t…
RepVideo: Rethinking Cross-Layer Representation for Video Generation
Chenyang Si, Weichen Fan, Zhengyao Lv +3
Video generation has achieved remarkable progress with the introduction of diffusion models, which have significantly improved the quality of generated videos. However, recent rese…
Vchitect-2.0: Parallel Transformer for Scaling Up Video Diffusion Models
Weichen Fan, Chenyang Si, Junhao Song +16
We present Vchitect-2.0, a parallel transformer architecture designed to scale up video diffusion models for large-scale text-to-video generation. The overall Vchitect-2.0 system h…
Evaluation Agent: Efficient and Promptable Evaluation Framework for Visual Generative Models
Fan Zhang, Shulin Tian, Ziqi Huang +2
Recent advancements in visual generative models have enabled high-quality image and video generation, opening diverse applications. However, evaluating these models often demands s…
VBench++: Comprehensive and Versatile Benchmark Suite for Video Generative Models
Ziqi Huang, Fan Zhang, Xiaojie Xu +14
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable…
FreeInit: Bridging Initialization Gap in Video Diffusion Models
Tianxing Wu, Chenyang Si, Yuming Jiang +2
Though diffusion-based video generation has witnessed rapid progress, the inference results of existing models still exhibit unsatisfactory temporal consistency and unnatural dynam…
VBench: Comprehensive Benchmark Suite for Video Generative Models
Ziqi Huang, Yinan He, Jiashuo Yu +13
Video generation has witnessed significant advancements, yet evaluating these models remains a challenge. A comprehensive evaluation benchmark for video generation is indispensable…
FreeU: Free Lunch in Diffusion U-Net
Chenyang Si, Ziqi Huang, Yuming Jiang +1
In this paper, we uncover the untapped potential of diffusion U-Net, which serves as a "free lunch" that substantially improves the generation quality on the fly. We initially inve…