Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality

• URL: http://arxiv.org/abs/2512.07951v1
• Date: Mon, 08 Dec 2025 19:00:04 GMT
• Title: Preserving Source Video Realism: High-Fidelity Face Swapping for Cinematic Quality
• Authors: Zekai Luo, Zongze Du, Zhouhang Zhu, Hao Zhong, Muzhi Zhu, Wen Wang, Yuling Xi, Chenchen Jing, Hao Chen, Chunhua Shen,
• Abstract summary: We present LivingSwap, the first video reference guided face swapping model.<n>By combining video conditioning with video reference guidance, the model performs temporal stitching to ensure stable identity and high-fidelity reconstruction.<n>Our method integrates the target identity with the source video's expressions, lighting, and motion, while significantly reducing manual effort in production.
• Score: 48.231357260785195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Video face swapping is crucial in film and entertainment production, where achieving high fidelity and temporal consistency over long and complex video sequences remains a significant challenge. Inspired by recent advances in reference-guided image editing, we explore whether rich visual attributes from source videos can be similarly leveraged to enhance both fidelity and temporal coherence in video face swapping. Building on this insight, this work presents LivingSwap, the first video reference guided face swapping model. Our approach employs keyframes as conditioning signals to inject the target identity, enabling flexible and controllable editing. By combining keyframe conditioning with video reference guidance, the model performs temporal stitching to ensure stable identity preservation and high-fidelity reconstruction across long video sequences. To address the scarcity of data for reference-guided training, we construct a paired face-swapping dataset, Face2Face, and further reverse the data pairs to ensure reliable ground-truth supervision. Extensive experiments demonstrate that our method achieves state-of-the-art results, seamlessly integrating the target identity with the source video's expressions, lighting, and motion, while significantly reducing manual effort in production workflows. Project webpage: https://aim-uofa.github.io/LivingSwap

Related papers

• DreamID-V:Bridging the Image-to-Video Gap for High-Fidelity Face Swapping via Diffusion Transformer [21.788582116033684]
  Video Face Swapping (VFS) requires seamlessly injecting a source identity into a target video.<n>Existing methods struggle to maintain identity similarity and attribute preservation while preserving temporal consistency.<n>We propose a comprehensive framework to seamlessly transfer the superiority of Image Face Swapping to the video domain.
  arXiv  Detail & Related papers  (2026-01-04T08:07:11Z)
• AlcheMinT: Fine-grained Temporal Control for Multi-Reference Consistent Video Generation [58.844504598618094]
  We propose AlcheMinT, a unified framework that introduces explicit timestamps conditioning for subject-driven video generation.<n>Our approach introduces a novel positional encoding mechanism that unlocks the encoding of temporal intervals, associated in our case with subject identities.<n>We incorporate subject-descriptive text tokens to strengthen binding between visual identity and video captions, mitigating ambiguity during generation.
  arXiv  Detail & Related papers  (2025-12-11T18:59:34Z)
• DirectSwap: Mask-Free Cross-Identity Training and Benchmarking for Expression-Consistent Video Head Swapping [58.2549561389375]
  Video head swapping aims to replace the entire head of a video subject, including facial identity, head shape, and hairstyle, with that of a reference image.<n>Due to the lack of ground-truth paired swapping data, prior methods typically train on cross-frame pairs of the same person within a video.<n>We propose DirectSwap, a mask-free, direct video head-swapping framework that extends an image U-Net into a video diffusion model.
  arXiv  Detail & Related papers  (2025-12-10T08:31:28Z)
• Identity-Preserving Image-to-Video Generation via Reward-Guided Optimization [38.70220886362519]
  We propose Identity-Preserving Reward-guided Optimization (IPRO) for image-to-video (I2V) generation.<n>IPRO is a novel video diffusion framework based on reinforcement learning to enhance identity preservation.<n>Our method backpropagates the reward signal through the last steps of the sampling chain, enabling richer feedback.
  arXiv  Detail & Related papers  (2025-10-16T03:13:47Z)
• VividFace: High-Quality and Efficient One-Step Diffusion For Video Face Enhancement [51.83206132052461]
  Video Face Enhancement (VFE) seeks to reconstruct high-quality facial regions from degraded video sequences.<n>Current methods that rely on video super-resolution and generative frameworks face three fundamental challenges.<n>We propose VividFace, a novel and efficient one-step diffusion framework for video face enhancement.
  arXiv  Detail & Related papers  (2025-09-28T02:39:48Z)
• From Large Angles to Consistent Faces: Identity-Preserving Video Generation via Mixture of Facial Experts [69.44297222099175]
  We introduce a Mixture of Facial Experts (MoFE) that captures distinct but mutually reinforcing aspects of facial attributes.<n>To mitigate dataset limitations, we have tailored a data processing pipeline centered on two key aspects: Face Constraints and Identity Consistency.<n>We have curated and refined a Large Face Angles (LFA) dataset from existing open-source human video datasets.
  arXiv  Detail & Related papers  (2025-08-13T04:10:16Z)
• VividFace: A Diffusion-Based Hybrid Framework for High-Fidelity Video Face Swapping [43.30061680192465]
  We present the first diffusion-based framework specifically designed for video face swapping.<n>Our approach incorporates a specially designed diffusion model coupled with a VidFaceVAE.<n>Our framework achieves superior performance in identity preservation, temporal consistency, and visual quality compared to existing methods.
  arXiv  Detail & Related papers  (2024-12-15T18:58:32Z)
• PersonalVideo: High ID-Fidelity Video Customization without Dynamic and Semantic Degradation [36.21554597804604]
  Identity-specific human video generation with customized ID images is still under-explored.<n>Key challenge lies in maintaining high ID fidelity consistently while preserving the original motion dynamic and semantic following.<n>We propose a novel framework, dubbed $textbfPersonalVideo$, that applies a mixture of reward supervision on synthesized videos.
  arXiv  Detail & Related papers  (2024-11-26T02:25:38Z)
• WildVidFit: Video Virtual Try-On in the Wild via Image-Based Controlled Diffusion Models [132.77237314239025]
  Video virtual try-on aims to generate realistic sequences that maintain garment identity and adapt to a person's pose and body shape in source videos. Traditional image-based methods, relying on warping and blending, struggle with complex human movements and occlusions. We reconceptualize video try-on as a process of generating videos conditioned on garment descriptions and human motion. Our solution, WildVidFit, employs image-based controlled diffusion models for a streamlined, one-stage approach.
  arXiv  Detail & Related papers  (2024-07-15T11:21:03Z)
• VividPose: Advancing Stable Video Diffusion for Realistic Human Image Animation [79.99551055245071]
  We propose VividPose, an end-to-end pipeline that ensures superior temporal stability. An identity-aware appearance controller integrates additional facial information without compromising other appearance details. A geometry-aware pose controller utilizes both dense rendering maps from SMPL-X and sparse skeleton maps. VividPose exhibits superior generalization capabilities on our proposed in-the-wild dataset.
  arXiv  Detail & Related papers  (2024-05-28T13:18:32Z)
• Video Demoireing with Relation-Based Temporal Consistency [68.20281109859998]
  Moire patterns, appearing as color distortions, severely degrade image and video qualities when filming a screen with digital cameras. We study how to remove such undesirable moire patterns in videos, namely video demoireing.
  arXiv  Detail & Related papers  (2022-04-06T17:45:38Z)
• Encode-in-Style: Latent-based Video Encoding using StyleGAN2 [0.7614628596146599]
  We propose an end-to-end facial video encoding approach that facilitates data-efficient high-quality video re-synthesis. The approach builds on StyleGAN2 image inversion and multi-stage non-linear latent-space editing to generate videos that are nearly comparable to input videos.
  arXiv  Detail & Related papers  (2022-03-28T05:44:19Z)

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.