V2V-Bench is a new 11-dimension benchmark for video-to-video generation that achieves 0.905 Spearman correlation with human judgments on six V2V-specific dimensions.
Controlvideo: Adding conditional control for one shot text-to-video editing
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.CV 3roles
background 1polarities
background 1representative citing papers
LooseControlVideo fine-tunes a video model on DNOCS-annotated data to enable layout and trajectory control via oriented 3D boxes, reporting 1.2-3x gains in trajectory accuracy over 2D baselines on nuScenes, HO-3D and BEHAVE.
VBench-2.0 is a benchmark suite that automatically evaluates video generative models on five dimensions of intrinsic faithfulness: Human Fidelity, Controllability, Creativity, Physics, and Commonsense using VLMs, LLMs, and anomaly detection methods.
citing papers explorer
-
V2V-Bench: A Comprehensive Benchmark for Video-to-Video Generation Evaluation
V2V-Bench is a new 11-dimension benchmark for video-to-video generation that achieves 0.905 Spearman correlation with human judgments on six V2V-specific dimensions.
-
LooseControlVideo: Directorial Video Control using Spatial Blocking
LooseControlVideo fine-tunes a video model on DNOCS-annotated data to enable layout and trajectory control via oriented 3D boxes, reporting 1.2-3x gains in trajectory accuracy over 2D baselines on nuScenes, HO-3D and BEHAVE.
-
VBench-2.0: Advancing Video Generation Benchmark Suite for Intrinsic Faithfulness
VBench-2.0 is a benchmark suite that automatically evaluates video generative models on five dimensions of intrinsic faithfulness: Human Fidelity, Controllability, Creativity, Physics, and Commonsense using VLMs, LLMs, and anomaly detection methods.