VioPose: Violin Performance 4D Pose Estimation by Hierarchical Audiovisual Inference

• URL: http://arxiv.org/abs/2411.13607v1
• Date: Tue, 19 Nov 2024 20:57:15 GMT
• Title: VioPose: Violin Performance 4D Pose Estimation by Hierarchical Audiovisual Inference
• Authors: Seong Jong Yoo, Snehesh Shrestha, Irina Muresanu, Cornelia Fermüller,
• Abstract summary: Current state-of-the-art visual pose estimation algorithms struggle to produce accurate monocular 4D poses. We propose VioPose: a novel multimodal network that hierarchically estimates dynamics. Our architecture is shown to produce accurate pose sequences, facilitating precise motion analysis, and outperforms SoTA.
• Score: 7.5565058831496055
• License:
• Abstract: Musicians delicately control their bodies to generate music. Sometimes, their motions are too subtle to be captured by the human eye. To analyze how they move to produce the music, we need to estimate precise 4D human pose (3D pose over time). However, current state-of-the-art (SoTA) visual pose estimation algorithms struggle to produce accurate monocular 4D poses because of occlusions, partial views, and human-object interactions. They are limited by the viewing angle, pixel density, and sampling rate of the cameras and fail to estimate fast and subtle movements, such as in the musical effect of vibrato. We leverage the direct causal relationship between the music produced and the human motions creating them to address these challenges. We propose VioPose: a novel multimodal network that hierarchically estimates dynamics. High-level features are cascaded to low-level features and integrated into Bayesian updates. Our architecture is shown to produce accurate pose sequences, facilitating precise motion analysis, and outperforms SoTA. As part of this work, we collected the largest and the most diverse calibrated violin-playing dataset, including video, sound, and 3D motion capture poses. Project page: is available at https://sj-yoo.info/viopose/.

Related papers

• DreamScene4D: Dynamic Multi-Object Scene Generation from Monocular Videos [21.93514516437402]
  We present DreamScene4D, the first approach to generate 3D dynamic scenes of multiple objects from monocular videos via novel view synthesis. Our key insight is a "decompose-recompose" approach that factorizes the video scene into the background and object tracks. We show extensive results on challenging DAVIS, Kubric, and self-captured videos with quantitative comparisons and a user preference study.
  arXiv  Detail & Related papers  (2024-05-03T17:55:34Z)
• DanceCamera3D: 3D Camera Movement Synthesis with Music and Dance [50.01162760878841]
  We present DCM, a new multi-modal 3D dataset that combines camera movement with dance motion and music audio. This dataset encompasses 108 dance sequences (3.2 hours) of paired dance-camera-music data from the anime community. We propose DanceCamera3D, a transformer-based diffusion model that incorporates a novel body attention loss and a condition separation strategy.
  arXiv  Detail & Related papers  (2024-03-20T15:24:57Z)
• DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos [76.01906393673897]
  We propose a self-supervised method to jointly learn 3D motion and depth from monocular videos. Our system contains a depth estimation module to predict depth, and a new decomposed object-wise 3D motion (DO3D) estimation module to predict ego-motion and 3D object motion. Our model delivers superior performance in all evaluated settings.
  arXiv  Detail & Related papers  (2024-03-09T12:22:46Z)
• Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Image [59.18564636990079]
  We study the problem of synthesizing a long-term dynamic video from only a single image. Existing methods either hallucinate inconsistent perpetual views or struggle with long camera trajectories. We present Make-It-4D, a novel method that can generate a consistent long-term dynamic video from a single image.
  arXiv  Detail & Related papers  (2023-08-20T12:53:50Z)
• BRACE: The Breakdancing Competition Dataset for Dance Motion Synthesis [123.73677487809418]
  We introduce a new dataset aiming to challenge common assumptions in dance motion synthesis. We focus on breakdancing which features acrobatic moves and tangled postures. Our efforts produced the BRACE dataset, which contains over 3 hours and 30 minutes of densely annotated poses.
  arXiv  Detail & Related papers  (2022-07-20T18:03:54Z)
• 3D Moments from Near-Duplicate Photos [67.15199743223332]
  3D Moments is a new computational photography effect. We produce a video that smoothly interpolates the scene motion from the first photo to the second. Our system produces photorealistic space-time videos with motion parallax and scene dynamics.
  arXiv  Detail & Related papers  (2022-05-12T17:56:18Z)
• AIMusicGuru: Music Assisted Human Pose Correction [8.020211030279686]
  We present a method that leverages our understanding of the high degree of a causal relationship between the sound produced and the motion that produces them. We use the audio signature to refine and predict accurate human body pose motion models. We also open-source MAPdat, a new multi-modal dataset of 3D violin playing motion with music.
  arXiv  Detail & Related papers  (2022-03-24T03:16:42Z)
• Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition [13.289339907084424]
  We propose a Hierarchical Dance Video Recognition framework (HDVR) HDVR estimates 2D pose sequences, tracks dancers, and then simultaneously estimates corresponding 3D poses and 3D-to-2D imaging parameters. From the estimated 3D pose sequence, HDVR extracts body part movements, and therefrom dance genre.
  arXiv  Detail & Related papers  (2021-09-19T16:59:37Z)
• Learning Motion Priors for 4D Human Body Capture in 3D Scenes [81.54377747405812]
  We propose LEMO: LEarning human MOtion priors for 4D human body capture. We introduce a novel motion prior, which reduces the jitters exhibited by poses recovered over a sequence. We also design a contact friction term and a contact-aware motion infiller obtained via per-instance self-supervised training. With our pipeline, we demonstrate high-quality 4D human body capture, reconstructing smooth motions and physically plausible body-scene interactions.
  arXiv  Detail & Related papers  (2021-08-23T20:47:09Z)

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.