YingVideo-MV: Music-Driven Multi-Stage Video Generation

• Existing music-driven video methods often lack cinematographic modeling and multi-camera control, while frame-wise long-sequence generation can degrade temporal coherence and identity consistency.The introduction identifies composition, beat coordination, camera movement, and long-duration consistency as challenges.Source: §1 · p. 2
• MV-Director converts multimodal inputs into structured shot plans with initial-frame composition, beat cues, and character-motion patterns, while the framework also establishes a large-scale Music-in-the-Wild Dataset.Source: §1 · pp. 2–3
• The two-stage pipeline uses MV-Director for high-level shot planning, then a temporal-aware diffusion Transformer for shot animation, balancing global consistency with local expressiveness.The model jointly handles lip-sync, facial expressions, and camera motion; dynamic-window inference propagates visual states across segments, and DPO training improves lip synchronization and visual quality.Source: §1 · p. 3
• YingVideo-MV generates high-fidelity portrait videos with synchronized camera movement, precise lip-sync, and varied facial expressions across diverse scenarios.The authors also report consistent character portrayals and camera movements synchronized with musical beats.Source: §1 · p. 3

2 Related Work

• The review organizes prior work around audio-driven video generation, controllable camera poses, and joint audio-visual camera-trajectory generation.Source: §2 · p. 3

2.1 Audio-driven Video Generation

• Audio-driven video methods progressed from U-Nets to DiTs, but the review identifies weak audio-visual semantic alignment and fixed-perspective constraints as persistent limitations.The passage also notes that fixed perspectives constrain occlusion handling and view-dependent deformations under camera movement.Source: §2.1 · p. 3

2.2 Controllable Camera Poses

• Camera-control approaches include direct camera-parameter conditioning, 4D scene modeling, and two-stage motion pipelines, yet the review reports camera jumps in generated videos.Source: §2.2 · p. 4

2.3 Joint Audio-visual Generation of Camera Trajectories

• Prior trajectory methods include reconstructed 3D/4D representations and pose-agnostic control, while the review says existing approaches assume trajectories align with the first frame.Source: §2.3 · p. 4

3.1 Preliminaries

• Flow matching trains a denoiser to predict the target velocity between Gaussian noise and sample latents, with audio, images, video references, text, and camera poses as conditions.The framework learns to transform noised camera-pose sequences into structured dynamic trajectories through a dedicated denoising process.Source: §3.1 · pp. 4–5
• Camera poses are represented as per-pixel Plücker embeddings derived from camera extrinsics and intrinsics, producing six-channel spatial maps for each frame.The representation encodes camera rays using the camera center and normalized ray direction.Source: §3.1 · p. 5

3.2 Cascaded Generation Pipeline

• MV-Director treats video creation as sequential tool use, combining music analysis and segmentation with scene planning, camera-trajectory generation, visual synthesis, and editing.Music clips are segmented around beat-onset peaks to approximate one bar; music understanding extracts transcription and emotional attributes for scene-level scripts.Source: §3.2 · pp. 5–7

3.3 Model Architecture

• The S2V model sends Wav2Vec audio embeddings through an audio adapter into the denoising DiT, while reference images condition generation through latent and CLIP pathways.The latent pathway concatenates encoded reference-image latents with compressed video frames and masks; CLIP image embeddings enter image-audio cross-attention blocks.Source: §3.3 · p. 7
• Camera poses use per-pixel Plücker embeddings projected to the noisy-latent shape and added to the latent before DiT processing.The embedding encodes pose from camera extrinsics and intrinsics; the adapter uses PixelUnshuffle, Conv2d, and ResidualBlock layers.Source: §3.1 · p. 5, §3.3 · p. 7

3.4 Direct Preference Optimization

• For preference training, four clips are scored with Sync-C, hand-quality reward, and VideoReward; the highest- and lowest-scoring clips become preferred and dispreferred samples.The DPO objective raises preferred-output likelihood while regularizing deviation from a reference policy; its flow-based form adjusts denoising toward high-reward trajectories while preserving reference temporal dynamics.Source: §3.4 · p. 8

3.5 Timestep-aware Dynamic Window Range Strategy

• At each denoising timestep, overlapping windows shift while first clips reset and last clips expand when they approach minimum lengths, avoiding tiny boundary windows.Unlike Stable Avatar, the method shifts starting positions; unlike Sonic, it keeps clips overlapping and does not roll the final frames into the initial clip.Source: Figure 4 · p. 8, §3.5 · p. 9
• Figure 5 qualitatively shows generated camera motions aligned with body movements.The reported examples include camera zoom-out with spatially coherent scene expansion and zoom-in while preserving character identity.Source: Figure 5 · p. 10

4 Experiments

• Evaluation spans HDTF, CelebV-HQ, EMTD, and MultiCamVideo, using automated metrics and ratings from 20 participants.Training used approximately 1,500 hours of general performance video for stage 1 and 400 hours of music-performance video for stage 2.Source: §4 · p. 10

4.1 Experiment Results

• Across camera controls, zoom-out scene expansion preserves spatial coherence and plausible out-of-frame content, while zoom-in retains character identity and fine scene details.The authors also report visual naturalness across evaluated camera motions, including transitions between dynamic and static shots.Source: §4.1 · p. 11
• YingVideo-MV reports significantly better visual quality and identity preservation, and lower translation error than other baselines, although it does not achieve the minimum rotation error.Compared with InfiniteTalk, it reports reduced FID and FVD scores, which the authors attribute to camera movement; StableAvatar and InfiniteTalk lack camera-motion metrics.Source: §4.1 · p. 11, Table 1 · p. 11
• 4.3±0.6 for camera motion and 4.5±0.5 for lip-sync were among the user-study averages; the method was significantly superior across all four rated dimensions.The other averages were 4.2±0.5 for motion naturalness and 4.4±0.6 for overall quality.Source: §4.1 · p. 11, Table 2 · p. 11

4.2 Ablation Study

• DPO improves all reported metrics, while the dynamic-window strategy yields a 6.3% improvement in temporal smoothness (FVD↓).The study evaluates models with and without DPO and dynamic-window inference.Source: §4.2 · p. 13, Table 3 · p. 13

5 Limitation and Future Work

• The model struggles with non-human references whose morphology and structure differ substantially from human subjects.The paper attributes this boundary to geometric and textural complexity beyond its current capacity for novel-shape synthesis.Source: §5 · p. 13
• An auxiliary reference-aware network is proposed to capture semantic details through hierarchical feature adaptation and cross-domain knowledge transfer.Source: §5 · p. 13
• Supporting multi-character interactive music videos remains future work, requiring advances in interpersonal spatial reasoning and behavioral coordination while maintaining audio-visual synchronization.Source: §5 · p. 13

6 Conclusion

• Human-preference metrics validate YingVideo-MV’s superiority over baseline methods.Source: §6 · p. 13
• The two-stage pipeline uses MV-Director for global shot planning, then DiT-based clip-wise generation for high-resolution video details.Source: §6 · p. 13
• Dynamic-window inference refines inter-clip visual transitions, while the framework preserves global semantic alignment and fine-grained audio-visual details.The conclusion also reports precise lip synchronization, identity consistency, and controllable camera dynamics.Source: §6 · p. 13