Tora3: Trajectory-Guided Audio-Video Generation with Physical Coherence
- URL: http://arxiv.org/abs/2604.09057v2
- Date: Thu, 16 Apr 2026 03:03:01 GMT
- Title: Tora3: Trajectory-Guided Audio-Video Generation with Physical Coherence
- Abstract summary: Tora3 is a trajectory-guided AV generation framework that improves physical coherence by using object trajectories as a shared kinematic prior.<n>Specifically, we design a trajectory-aligned motion representation for video, a kinematic-audio alignment module driven by trajectory-derived second-order kinematic states, and a hybrid flow matching scheme.<n>Extensive experiments show that Tora3 improves motion realism, motion-sound synchronization, and overall AV generation quality over strong open-source baselines.
- Score: 16.685702628989212
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Audio-video (AV) generation has recently made strong progress in perceptual quality and multimodal coherence, yet generating content with plausible motion-sound relations remains challenging. Existing methods often produce object motions that are visually unstable and sounds that are only loosely aligned with salient motion or contact events, largely because they lack an explicit motion-aware structure shared by video and audio generation. We present Tora3, a trajectory-guided AV generation framework that improves physical coherence by using object trajectories as a shared kinematic prior. Rather than treating trajectories as a video-only control signal, Tora3 uses them to jointly guide visual motion and acoustic events. Specifically, we design a trajectory-aligned motion representation for video, a kinematic-audio alignment module driven by trajectory-derived second-order kinematic states, and a hybrid flow matching scheme that preserves trajectory fidelity in trajectory-conditioned regions while maintaining local coherence elsewhere. We further curate PAV, a large-scale AV dataset emphasizing motion-relevant patterns with automatically extracted motion annotations. Extensive experiments show that Tora3 improves motion realism, motion-sound synchronization, and overall AV generation quality over strong open-source baselines.
Related papers
- EchoWM: Open and Enterable Omnimodal World Models [71.49148067556887]
EchoWM is an omnimodal world model for enterable generative media.<n>It responds to continuous navigation while jointly generating 720p video, environmental sound, music and speech.
arXiv Detail & Related papers (2026-08-24T12:39:59Z) - InfiniVerse: Occupancy Guided Unbounded Scene Generation for Autonomous Driving [70.7737035270887]
We introduce InfiniVerse, a unified pipeline for synthesis of dynamic urban scenes from a single frame.<n>InfiniVerse achieves state-of-the-art performance with a FID of 6.4 and FVD of 67.97, significantly outperforming existing benchmarks in both duration and stability.
arXiv Detail & Related papers (2026-06-30T04:08:22Z) - Follow Your Track: Precise Skeleton Animation Controlled by 3D Trajectories [93.39170866007431]
4D generation aims to animate 3D objects with realistic motion, holding great promise for applications.<n>Existing methods typically decouple 3D asset generation from motion synthesis.<n>We present textttACT, a trajectory-conditioned framework for topology-general skeletal animation.
arXiv Detail & Related papers (2026-06-24T03:18:10Z) - MotionAdapter: Video Motion Transfer via Content-Aware Attention Customization [73.07309070257162]
MotionAdapter is a content-aware motion transfer framework that enables robust and semantically aligned motion transfer.<n>Our key insight is that effective motion transfer requires explicit disentanglement of motion from appearance.<n> MotionAdapter naturally supports complex motion transfer and motion editing tasks such as zooming.
arXiv Detail & Related papers (2026-01-05T10:01:27Z) - DragMesh: Interactive 3D Generation Made Easy [12.832539752284466]
DragMesh is a robust framework for real-time interactive 3D articulation.<n>Our core contribution is a novel decoupled kinematic reasoning and motion generation framework.
arXiv Detail & Related papers (2025-12-06T13:10:44Z) - DEMO: Disentangled Motion Latent Flow Matching for Fine-Grained Controllable Talking Portrait Synthesis [15.304037069236536]
DEMO is a flow-matching generative framework for audio-driven talking-head video synthesis.<n>It delivers disentangled, high-fidelity control of lip motion, head pose, and eye gaze.
arXiv Detail & Related papers (2025-10-12T15:10:33Z) - Zero-shot 3D-Aware Trajectory-Guided image-to-video generation via Test-Time Training [27.251232052868033]
Trajectory-Guided image-to-video (I2V) generation aims to synthesize videos that adhere to user-specified motion instructions.<n>Zo3T significantly enhances 3D realism and motion accuracy in trajectory-controlled I2V generation.
arXiv Detail & Related papers (2025-09-08T14:21:45Z) - Tracking the Unstable: Appearance-Guided Motion Modeling for Robust Multi-Object Tracking in UAV-Captured Videos [58.156141601478794]
Multi-object tracking (UAVT) aims to track multiple objects while maintaining consistent identities across frames of a given video.<n>Existing methods typically model motion cues and appearance separately, overlooking their interplay and resulting in suboptimal tracking performance.<n>We propose AMOT, which exploits appearance and motion cues through two key components: an Appearance-Motion Consistency (AMC) matrix and a Motion-aware Track Continuation (MTC) module.
arXiv Detail & Related papers (2025-08-03T12:06:47Z) - M2DAO-Talker: Harmonizing Multi-granular Motion Decoupling and Alternating Optimization for Talking-head Generation [65.48046909056468]
We reformulate talking head generation into a unified framework comprising video preprocessing, motion representation, and rendering reconstruction.<n>M2DAO-Talker achieves state-of-the-art performance, with the 2.43 dB PSNR improvement in generation quality and 0.64 gain in user-evaluated video realness.
arXiv Detail & Related papers (2025-07-11T04:48:12Z) - SViMo: Synchronized Diffusion for Video and Motion Generation in Hand-object Interaction Scenarios [48.09735396455107]
Hand-Object Interaction (HOI) generation has significant application potential.<n>Current 3D HOI motion generation approaches heavily rely on predefined 3D object models and lab-captured motion data.<n>We propose a novel framework that combines visual priors and dynamic constraints within a synchronized diffusion process to generate the HOI video and motion simultaneously.
arXiv Detail & Related papers (2025-06-03T05:04:29Z) - Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction [86.099855111676]
Traditional SLAM systems struggle with highly dynamic scenes commonly found in casual videos.<n>This work leverages a 3D point tracker to separate the camera-induced motion from the observed motion of dynamic objects.<n>Our framework combines the core of traditional SLAM -- bundle adjustment -- with a robust learning-based 3D tracker front-end.
arXiv Detail & Related papers (2025-04-20T07:29:42Z) - Tracktention: Leveraging Point Tracking to Attend Videos Faster and Better [61.381599921020175]
Temporal consistency is critical in video prediction to ensure that outputs are coherent and free of artifacts.<n>Traditional methods, such as temporal attention and 3D convolution, may struggle with significant object motion.<n>We propose the Tracktention Layer, a novel architectural component that explicitly integrates motion information using point tracks.
arXiv Detail & Related papers (2025-03-25T17:58:48Z) - C-Drag: Chain-of-Thought Driven Motion Controller for Video Generation [81.4106601222722]
Trajectory-based motion control has emerged as an intuitive and efficient approach for controllable video generation.<n>We propose a Chain-of-Thought-based motion controller for controllable video generation, named C-Drag.<n>Our method includes an object perception module and a Chain-of-Thought-based motion reasoning module.
arXiv Detail & Related papers (2025-02-27T08:21:03Z)
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.