What Are You Doing? A Closer Look at Controllable Human Video Generation
Not on the map. Why?
Abstract (arXiv)
High-quality benchmarks are crucial for driving progress in machine learning research. However, despite the growing interest in video generation, there is no comprehensive dataset to evaluate human synthesis. Humans can perform a wide variety of actions and interactions, but existing datasets, like TikTok, TED-Talks, and HumanVid, lack the diversity and complexity to fully capture the capabilities of video generation models. We close this gap by introducing 'What Are You Doing?' (WYD): a new benchmark for fine-grained evaluation of controllable image-to-video generation of humans. WYD consists of 1,544 captioned videos that have been meticulously collected and annotated with fine-grained categories. These allow us to systematically measure performance across 9 aspects of human generation, including actions, interactions and motion. We also propose an evaluation framework, where we adapt existing metrics for better human- and video-level assessment, as shown by human preference. Equipped with our dataset and metrics, we perform in-depth analyses of state-of-the-art open-source models in controllable image-to-video generation, showing how WYD provides novel insights about their capabilities. We release data and code to drive progress in human video generation.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 73 of its 89 references that are in the map. First posted on arXiv 6 Mar 2025.
Builds on
73 of 89 references.
References · 73
- 2020Denoising Diffusion Probabilistic Modelscited by 20,851
- 2014Microsoft COCO: Common Objects in Contextcited by 17,498
- 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
- 2023DINOv2: Learning Robust Visual Features without Supervisioncited by 7,764
- 2021Emerging Properties in Self-Supervised Vision Transformerscited by 6,893
- 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
- 2017Quo Vadis, Action Recognition? A New Model and the Kinetics Datasetcited by 3,259
- 2023Stable Video Diffusion: Scaling Latent Video Diffusion Models to Large Datasetscited by 2,526
- 2020RAFT: Recurrent All-Pairs Field Transforms for Optical Flowcited by 2,385
- 2022Video Diffusion Modelscited by 2,280
- 2024CogVideoX: Text-to-Video Diffusion Models with An Expert Transformercited by 2,198
- 2017The Kinetics Human Action Video Datasetcited by 1,894
38 more
- 2022Imagen Video: High Definition Video Generation with Diffusion Modelscited by 1,765
- 2023VBench: Comprehensive Benchmark Suite for Video Generative Modelscited by 1,755
- 2023Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Modelscited by 1,465
- 2021Frozen in Time: A Joint Video and Image Encoder for End-to-End Retrievalcited by 1,175
- 2018OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fieldscited by 1,161
- 2022Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generationcited by 958
- 2023Pick-a-Pic: An Open Dataset of User Preferences for Text-to-Image Generationcited by 950
- 2017The 2017 DAVIS Challenge on Video Object Segmentationcited by 942
- 2023Animate Anyone: Consistent and Controllable Image-to-Video Synthesis for Character Animationcited by 683
- 2017Localizing Moments in Video with Natural Languagecited by 660
- 2024Movie Gen: A Cast of Media Foundation Modelscited by 614
- 2023MotionCtrl: A Unified and Flexible Motion Controller for Video Generationcited by 568
- 2024Evaluating Text-to-Visual Generation with Image-to-Text Generationcited by 465
- 2024CameraCtrl: Enabling Camera Control for Text-to-Video Generationcited by 448
- 2024Panda-70M: Captioning 70M Videos with Multiple Cross-Modality Teacherscited by 389
- 2023Scaling Open-Vocabulary Object Detectioncited by 335
- 2023EvalCrafter: Benchmarking and Evaluating Large Video Generation Modelscited by 332
- 2023MagicAnimate: Temporally Consistent Human Image Animation using Diffusion Modelcited by 330
- 2021Multimodal datasets: misogyny, pornography, and malignant stereotypescited by 326
- 2023ControlVideo: Training-free Controllable Text-to-Video Generationcited by 324
- 2018Everybody Dance Nowcited by 313
- 2023Follow Your Pose: Pose-Guided Text-to-Video Generation using Pose-Free Videoscited by 312
- 2023Effective Whole-body Pose Estimation with Two-stages Distillationcited by 307
- 2021Motion Representations for Articulated Animationcited by 253
- 2017Pose Guided Person Image Generationcited by 251
- 2020Large image datasets: A pyrrhic win for computer vision?cited by 237
- 2024Motion-I2V: Consistent and Controllable Image-to-Video Generation with Explicit Motion Modelingcited by 209
- 2024MimicMotion: High-Quality Human Motion Video Generation with Confidence-aware Pose Guidancecited by 196
- 2024VideoScore: Building Automatic Metrics to Simulate Fine-grained Human Feedback for Video Generationcited by 190
- 2021Visually Grounded Reasoning across Languages and Culturescited by 188
- 2023Conditional Image-to-Video Generation with Latent Flow Diffusion Modelscited by 180
- 2024T2V-CompBench: A Comprehensive Benchmark for Compositional Text-to-video Generationcited by 177
- 2019Liquid Warping GAN: A Unified Framework for Human Motion Imitation, Appearance Transfer and Novel View Synthesiscited by 161
- 2024ControlNeXt: Powerful and Efficient Control for Image and Video Generationcited by 158
- 2023DisCo: Disentangled Control for Realistic Human Dance Generationcited by 141
- 2024ConsistI2V: Enhancing Visual Consistency for Image-to-Video Generationcited by 130
- 2023MagicPose: Realistic Human Poses and Facial Expressions Retargeting with Identity-aware Diffusioncited by 115
- 2024Boximator: Generating Rich and Controllable Motions for Video Synthesiscited by 107
50 most cited of 73 All 73 (JSON)
Built on by
Citing papers per year: 2025: 2; 2026: 1.
3 papers.