MV-Performer: Taming Video Diffusion Model for Faithful and Synchronized Multi-view Performer Synthesis

• URL: http://arxiv.org/abs/2510.07190v1
• Date: Wed, 08 Oct 2025 16:24:22 GMT
• Title: MV-Performer: Taming Video Diffusion Model for Faithful and Synchronized Multi-view Performer Synthesis
• Authors: Yihao Zhi, Chenghong Li, Hongjie Liao, Xihe Yang, Zhengwentai Sun, Jiahao Chang, Xiaodong Cun, Wensen Feng, Xiaoguang Han,
• Abstract summary: We present MV-Performer, an innovative framework for creating novel view videos from monocular full-body captures.<n>To achieve a 360-degree synthesis, we extensively leverage the MVHumanNet dataset and incorporate an informative condition signal.<n>To maintain synchronization in the generated videos, we propose a multi-view human-centric video diffusion model.
• Score: 34.793258395288895
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent breakthroughs in video generation, powered by large-scale datasets and diffusion techniques, have shown that video diffusion models can function as implicit 4D novel view synthesizers. Nevertheless, current methods primarily concentrate on redirecting camera trajectory within the front view while struggling to generate 360-degree viewpoint changes. In this paper, we focus on human-centric subdomain and present MV-Performer, an innovative framework for creating synchronized novel view videos from monocular full-body captures. To achieve a 360-degree synthesis, we extensively leverage the MVHumanNet dataset and incorporate an informative condition signal. Specifically, we use the camera-dependent normal maps rendered from oriented partial point clouds, which effectively alleviate the ambiguity between seen and unseen observations. To maintain synchronization in the generated videos, we propose a multi-view human-centric video diffusion model that fuses information from the reference video, partial rendering, and different viewpoints. Additionally, we provide a robust inference procedure for in-the-wild video cases, which greatly mitigates the artifacts induced by imperfect monocular depth estimation. Extensive experiments on three datasets demonstrate our MV-Performer's state-of-the-art effectiveness and robustness, setting a strong model for human-centric 4D novel view synthesis.

Related papers

• Human Video Generation from a Single Image with 3D Pose and View Control [62.676151243249556]
  We present Human Video Generation in 4D (HVG), a latent video diffusion model capable of generating high-quality multi-view,temporally coherent human videos from a single image.<n>HVG achieves this through three key designs: (i) Articulated Pose Modulation, which captures the anatomical relationships of 3D joints via a novel dual-dimensional bone map and resolves self-occlusions across views by introducing 3D information; (ii) View and Temporal Alignment, which ensures multi-view consistency and alignment between a reference image and pose sequences for frame-to-frame stability; and (iii)
  arXiv  Detail & Related papers  (2026-02-24T18:42:20Z)
• View-Consistent Diffusion Representations for 3D-Consistent Video Generation [60.68052293389281]
  Current generated videos still contain visual artifacts arising from 3D inconsistencies.<n>We propose ViCoDR, a new approach for improving the 3D consistency of video models by learning multi-view consistent diffusion representations.
  arXiv  Detail & Related papers  (2025-11-24T11:16:55Z)
• WorldSplat: Gaussian-Centric Feed-Forward 4D Scene Generation for Autonomous Driving [21.778139777889397]
  We propose textbfWorldSplat, a novel feed-forward framework for 4D driving-scene generation.<n>Our approach effectively generates consistent multi-track videos through two key steps.<n>Experiments conducted on benchmark datasets demonstrate that textbfWorldSplat effectively generates high-fidelity, temporally and spatially consistent novel view driving videos.
  arXiv  Detail & Related papers  (2025-09-27T16:47:44Z)
• Diffuman4D: 4D Consistent Human View Synthesis from Sparse-View Videos with Spatio-Temporal Diffusion Models [83.76517697509156]
  This paper addresses the challenge of high-fidelity view synthesis of humans with sparse-view videos as input.<n>We propose a novel iterative sliding denoising process to enhance view-temporal consistency of the 4D diffusion model.<n>Our method is able to synthesize high-quality and consistent novel-view videos and significantly outperforms the existing approaches.
  arXiv  Detail & Related papers  (2025-07-17T17:59:17Z)
• Zero4D: Training-Free 4D Video Generation From Single Video Using Off-the-Shelf Video Diffusion [52.0192865857058]
  We propose the first training-free 4D video generation method that leverages the off-the-shelf video diffusion models to generate multi-view videos from a single input video.<n>Our method is training-free and fully utilizes an off-the-shelf video diffusion model, offering a practical and effective solution for multi-view video generation.
  arXiv  Detail & Related papers  (2025-03-28T17:14:48Z)
• TrajectoryCrafter: Redirecting Camera Trajectory for Monocular Videos via Diffusion Models [33.219657261649324]
  TrajectoryCrafter is a novel approach to redirect camera trajectories for monocular videos.<n>By disentangling deterministic view transformations from content generation, our method achieves precise control over user-specified camera trajectories.
  arXiv  Detail & Related papers  (2025-03-07T17:57:53Z)
• ViewCrafter: Taming Video Diffusion Models for High-fidelity Novel View Synthesis [63.169364481672915]
  We propose textbfViewCrafter, a novel method for synthesizing high-fidelity novel views of generic scenes from single or sparse images. Our method takes advantage of the powerful generation capabilities of video diffusion model and the coarse 3D clues offered by point-based representation to generate high-quality video frames.
  arXiv  Detail & Related papers  (2024-09-03T16:53:19Z)
• SV4D: Dynamic 3D Content Generation with Multi-Frame and Multi-View Consistency [37.96042037188354]
  We present Stable Video 4D (SV4D), a latent video diffusion model for multi-frame and multi-view consistent dynamic 3D content generation.
  arXiv  Detail & Related papers  (2024-07-24T17:59:43Z)
• 4Diffusion: Multi-view Video Diffusion Model for 4D Generation [55.82208863521353]
  Current 4D generation methods have achieved noteworthy efficacy with the aid of advanced diffusion generative models. We propose a novel 4D generation pipeline, namely 4Diffusion, aimed at generating spatial-temporally consistent 4D content from a monocular video.
  arXiv  Detail & Related papers  (2024-05-31T08:18:39Z)
• MVD-Fusion: Single-view 3D via Depth-consistent Multi-view Generation [54.27399121779011]
  We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. We show that our approach can yield more accurate synthesis compared to recent state-of-the-art, including distillation-based 3D inference and prior multi-view generation methods.
  arXiv  Detail & Related papers  (2024-04-04T17:59:57Z)

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.