# LumiSculpt: Enabling Consistent Portrait Lighting in Video Generation

arXiv: https://arxiv.org/abs/2410.22979 · HTML: https://mlsanity.com/paper/2410.22979/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 38 references that are in the map.
- First posted on arXiv 30 Oct 2024.

## Builds on (most cited first, 25 of 25)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization](https://mlsanity.com/paper/1703.06868/) (2017)
- [AnimateDiff: Animate Your Personalized Text-to-Image Diffusion Models without Specific Tuning](https://mlsanity.com/paper/2307.04725/) (2023)
- [Tune-A-Video: One-Shot Tuning of Image Diffusion Models for Text-to-Video Generation](https://mlsanity.com/paper/2212.11565/) (2022)
- [ModelScope Text-to-Video Technical Report](https://mlsanity.com/paper/2308.06571/) (2023)
- [VideoCrafter1: Open Diffusion Models for High-Quality Video Generation](https://mlsanity.com/paper/2310.19512/) (2023)
- [VideoComposer: Compositional Video Synthesis with Motion Controllability](https://mlsanity.com/paper/2306.02018/) (2023)
- [CameraCtrl: Enabling Camera Control for Text-to-Video Generation](https://mlsanity.com/paper/2404.02101/) (2024)
- [Pix2Video: Video Editing using Image Diffusion](https://mlsanity.com/paper/2303.12688/) (2023)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [StableVideo: Text-driven Consistency-aware Diffusion Video Editing](https://mlsanity.com/paper/2308.09592/) (2023)
- [MotionDirector: Motion Customization of Text-to-Video Diffusion Models](https://mlsanity.com/paper/2310.08465/) (2023)
- [DiLightNet: Fine-grained Lighting Control for Diffusion-based Image Generation](https://mlsanity.com/paper/2402.11929/) (2024)
- [Learning Physics-guided Face Relighting under Directional Light](https://mlsanity.com/paper/1906.03355/) (2019)
- [Animate-A-Story: Storytelling with Retrieval-Augmented Video Generation](https://mlsanity.com/paper/2307.06940/) (2023)
- [Neural Light Transport for Relighting and View Synthesis](https://mlsanity.com/paper/2008.03806/) (2020)
- [TaleCrafter: Interactive Story Visualization with Multiple Characters](https://mlsanity.com/paper/2305.18247/) (2023)
- [Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation](https://mlsanity.com/paper/2209.10510/) (2022)
- [SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting](https://mlsanity.com/paper/2402.18848/) (2024)
- [LightIt: Illumination Modeling and Control for Diffusion Models](https://mlsanity.com/paper/2403.10615/) (2024)
- [Face Relighting with Geometrically Consistent Shadows](https://mlsanity.com/paper/2203.16681/) (2022)
- [Towards High Fidelity Face Relighting with Realistic Shadows](https://mlsanity.com/paper/2104.00825/) (2021)
- [OpenIllumination: A Multi-Illumination Dataset for Inverse Rendering Evaluation on Real Objects](https://mlsanity.com/paper/2309.07921/) (2023)
- [SunStage: Portrait Reconstruction and Relighting using the Sun as a Light Stage](https://mlsanity.com/paper/2204.03648/) (2022)
- [Retinex-Diffusion: On Controlling Illumination Conditions in Diffusion Models via Retinex Theory](https://mlsanity.com/paper/2407.20785/) (2024)

## Built on by (most cited first, 3 of 3)

- [Exploring the Evolution of Physics Cognition in Video Generation: A Survey](https://mlsanity.com/paper/2503.21765/) (2025)
- [Lumen: Consistent Video Relighting and Harmonious Background Replacement with Video Generative Models](https://mlsanity.com/paper/2508.12945/) (2025)
- [Measured Sliders: Learning Continuous Controls from Differentiable Image Measurements](https://mlsanity.com/paper/2609.05234/) (2026)

## Abstract (arXiv)

Lighting plays a pivotal role in ensuring the naturalness and aesthetic quality of video generation. However, the impact of lighting is deeply coupled with other factors of videos, e.g., objects and scenes. Thus, it remains challenging to disentangle and model coherent lighting conditions independently, limiting the flexibility to control lighting in video generation. In this paper, inspired by the established controllable T2I models, we propose LumiSculpt, which enables precise and consistent lighting control in T2V generation models. LumiSculpt equips the video generation with new interactive capabilities, allowing the input of reference image sequences with customized lighting conditions. Furthermore, the core learnable plug-and-play module of LumiSculpt facilitates direct control over the intensity, position and trajectory of an assumed light source in video diffusion models. To effectively train LumiSculpt and address the issue of insufficient lighting data, we construct LumiHuman, a new lightweight and flexible dataset for portrait lighting of images and videos. Experimental results demonstrate that LumiSculpt achieves precise and high-quality lighting control in video generation. The analysis demonstrates the flexibility of LumiHuman.