Personalized Video Relighting With an At-Home Light Stage

• URL: http://arxiv.org/abs/2311.08843v4
• Date: Fri, 27 Sep 2024 09:56:47 GMT
• Title: Personalized Video Relighting With an At-Home Light Stage
• Authors: Jun Myeong Choi, Max Christman, Roni Sengupta,
• Abstract summary: We develop a personalized video relighting algorithm that produces high-quality and temporally consistent relit videos in real-time. We show that by just capturing recordings of a user watching YouTube videos on a monitor we can train a personalized algorithm capable of performing high-quality relighting under any condition.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we develop a personalized video relighting algorithm that produces high-quality and temporally consistent relit videos under any pose, expression, and lighting condition in real-time. Existing relighting algorithms typically rely either on publicly available synthetic data, which yields poor relighting results, or on actual light stage data which is difficult to acquire. We show that by just capturing recordings of a user watching YouTube videos on a monitor we can train a personalized algorithm capable of performing high-quality relighting under any condition. Our key contribution is a novel image-based neural relighting architecture that effectively separates the intrinsic appearance features - the geometry and reflectance of the face - from the source lighting and then combines them with the target lighting to generate a relit image. This neural architecture enables smoothing of intrinsic appearance features leading to temporally stable video relighting. Both qualitative and quantitative evaluations show that our architecture improves portrait image relighting quality and temporal consistency over state-of-the-art approaches on both casually captured `Light Stage at Your Desk' (LSYD) and light-stage-captured `One Light At a Time' (OLAT) datasets.

Related papers

• LumiSculpt: A Consistency Lighting Control Network for Video Generation [67.48791242688493]
  Lighting plays a pivotal role in ensuring the naturalness of video generation. It remains challenging to disentangle and model independent and coherent lighting attributes. LumiSculpt enables precise and consistent lighting control in T2V generation models.
  arXiv  Detail & Related papers  (2024-10-30T12:44:08Z)
• Real-time 3D-aware Portrait Video Relighting [89.41078798641732]
  We present the first real-time 3D-aware method for relighting in-the-wild videos of talking faces based on Neural Radiance Fields (NeRF) We infer an albedo tri-plane, as well as a shading tri-plane based on a desired lighting condition for each video frame with fast dual-encoders. Our method runs at 32.98 fps on consumer-level hardware and achieves state-of-the-art results in terms of reconstruction quality, lighting error, lighting instability, temporal consistency and inference speed.
  arXiv  Detail & Related papers  (2024-10-24T01:34:11Z)
• BVI-RLV: A Fully Registered Dataset and Benchmarks for Low-Light Video Enhancement [56.97766265018334]
  This paper introduces a low-light video dataset, consisting of 40 scenes with various motion scenarios under two distinct low-lighting conditions. We provide fully registered ground truth data captured in normal light using a programmable motorized dolly and refine it via an image-based approach for pixel-wise frame alignment across different light levels. Our experimental results demonstrate the significance of fully registered video pairs for low-light video enhancement (LLVE) and the comprehensive evaluation shows that the models trained with our dataset outperform those trained with the existing datasets.
  arXiv  Detail & Related papers  (2024-07-03T22:41:49Z)
• BVI-Lowlight: Fully Registered Benchmark Dataset for Low-Light Video Enhancement [44.1973928137492]
  This paper introduces a novel low-light video dataset, consisting of 40 scenes in various motion scenarios under two low-lighting conditions. We provide fully registered ground truth data captured in normal light using a programmable motorized dolly. We refine them via image-based post-processing to ensure the pixel-wise alignment of frames in different light levels.
  arXiv  Detail & Related papers  (2024-02-03T00:40:22Z)
• Relightable Neural Actor with Intrinsic Decomposition and Pose Control [80.06094206522668]
  We propose Relightable Neural Actor, a new video-based method for learning a pose-driven neural human model that can be relighted. For training, our method solely requires a multi-view recording of the human under a known, but static lighting condition. To evaluate our approach in real-world scenarios, we collect a new dataset with four identities recorded under different light conditions, indoors and outdoors.
  arXiv  Detail & Related papers  (2023-12-18T14:30:13Z)
• Spatiotemporally Consistent HDR Indoor Lighting Estimation [66.26786775252592]
  We propose a physically-motivated deep learning framework to solve the indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Our framework achieves photorealistic lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods.
  arXiv  Detail & Related papers  (2023-05-07T20:36:29Z)
• RelightableHands: Efficient Neural Relighting of Articulated Hand Models [46.60594572471557]
  We present the first neural relighting approach for rendering high-fidelity personalized hands that can be animated in real-time under novel illumination. Our approach adopts a teacher-student framework, where the teacher learns appearance under a single point light from images captured in a light-stage. Using images rendered by the teacher model as training data, an efficient student model directly predicts appearance under natural illuminations in real-time.
  arXiv  Detail & Related papers  (2023-02-09T18:59:48Z)
• Learning to Relight Portrait Images via a Virtual Light Stage and Synthetic-to-Real Adaptation [76.96499178502759]
  Relighting aims to re-illuminate the person in the image as if the person appeared in an environment with the target lighting. Recent methods rely on deep learning to achieve high-quality results. We propose a new approach that can perform on par with the state-of-the-art (SOTA) relighting methods without requiring a light stage.
  arXiv  Detail & Related papers  (2022-09-21T17:15:58Z)
• Low-light Image and Video Enhancement via Selective Manipulation of Chromaticity [1.4680035572775534]
  We present a simple yet effective approach for low-light image and video enhancement. The above adaptivity allows us to avoid the costly step of low-light image decomposition into illumination and reflectance. Our results on standard lowlight image datasets show the efficacy of our algorithm and its qualitative and quantitative superiority over several state-of-the-art techniques.
  arXiv  Detail & Related papers  (2022-03-09T17:01:28Z)
• Spatio-Temporal Outdoor Lighting Aggregation on Image Sequences using Transformer Networks [23.6427456783115]
  In this work, we focus on outdoor lighting estimation by aggregating individual noisy estimates from images. Recent work based on deep neural networks has shown promising results for single image lighting estimation, but suffers from robustness. We tackle this problem by combining lighting estimates from several image views sampled in the angular and temporal domain of an image sequence.
  arXiv  Detail & Related papers  (2022-02-18T14:11:16Z)
• Neural Video Portrait Relighting in Real-time via Consistency Modeling [41.04622998356025]
  We propose a neural approach for real-time, high-quality and coherent video portrait relighting. We propose a hybrid structure and lighting disentanglement in an encoder-decoder architecture. We also propose a lighting sampling strategy to model the illumination consistency and mutation for natural portrait light manipulation in real-world.
  arXiv  Detail & Related papers  (2021-04-01T14:13:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.