5 citations · 13 across the 22 of their papers we have counts for
11 papers · 1 filter
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…