YingVideo-MV Music-Driven Video Generation

• YingVideo-MV is a cascaded music-driven video generation framework that integrates audio analysis, interpretable shot planning, and transformer-based diffusion to create coherent long videos.
• It employs multi-modal conditioning on audio, text, motion, and camera parameters to achieve precise synchronization and realistic, dynamic movements.
• The framework leverages a large-scale Music-in-the-Wild Dataset and advanced loss functions to deliver state-of-the-art metrics in audiovisual sync, motion realism, and temporal consistency.

YingVideo-MV is a cascaded music-driven video generation framework designed to synthesize long-form music performance videos with joint audio-visual-motion-camera control. It introduces the first architecture capable of generating coherent, expressive music videos from pure audio, explicitly controlling shot planning, performer motion, facial expression, and dynamic camera trajectories. This approach integrates audio semantic analysis, interpretable shot segmentation, transformer-based spatiotemporal diffusion modeling, and fine-grained camera control modules. Benchmarked on a large-scale, web-curated Music-in-the-Wild Dataset, YingVideo-MV demonstrates state-of-the-art metrics for audiovisual synchronization, motion realism, camera smoothness, and temporal consistency in long-video synthesis (Chen et al., 2 Dec 2025).

1. Problem Setting and Core Challenges

YingVideo-MV addresses the automatic generation of long-duration, music-driven performance videos of a single avatar (e.g., singer, instrumentalist), achieving frame-accurate synchronization among musical structure, body gestures, lip motions, and camera movements. The task’s central challenges are:

• Cinematographic language modeling: Preceding audio-driven video generations lack shot planning, multi-perspective framing, and camera motion (zoom, pan, depth changes) that correspond to musical phrasing and emotion. YingVideo-MV introduces modules for explicit shot planning and dynamic camera control.
• Cross-modal temporal alignment: Achieving beat-level precision in the alignment of audio, performer motion, lip sync, and camera transitions demands temporally resolved conditioning and segment-wise planning.
• Long-sequence coherence: Standard frame-by-frame or independently synthesized video clips often suffer from identity drift, pose inconsistencies, and motion discontinuities as sequence length increases. YingVideo-MV utilizes both global planning and overlapping temporal windows to address these issues (Chen et al., 2 Dec 2025).

2. Cascaded Framework Architecture

YingVideo-MV’s multi-stage pipeline is organized as follows:

1. Audio Semantic Analysis:
   • Beat/bar segmentation computes onset strength and segments music into bar-length intervals (Δ_bar = 4 × (60/bpm)).
   • Wav2Vec-based embeddings produce frame-wise audio representations $c_a^{[0:L]} \in \mathbb{R}^{L \times d_a}$ via a pre-trained encoder.
   • Semantic understanding uses a fine-tuned Qwen 2.5-Omni MLLM to extract a transcription (T) and emotion/style descriptors (E) for each segment, which inform downstream planning and synthesis modules.
2. MV-Director (Interpretable Shot Planning):
   • Translates user goals (narrative, style, identity), audio embeddings, and semantic features into a “shot list” $S = (s_1, ..., s_N)$, where each shot $s_i$ specifies temporal boundaries, a text prompt $P_i$, camera seed $C_i$, and an optional exemplar image $I_i$.
   • Implements an LLM-based agent acting on a toolbox of segmentation, semantic reasoning, trajectory generation, and prompt editing utilities.
3. Temporal-Aware Diffusion Transformer (Clip-Level Video Synthesis):
   • Employs a WAN 2.1 video diffusion backbone, extended with multi-modal conditioning:
     • CLIP text/image embeddings for style and identity injection.
     • Audio adapters mapping $c_a$ to transformer keys/values at each layer.
   • LoRA fine-tuning is used within attention layers for efficient adaptation to the music video domain, with parameter updates $W' = W + AB^T$.
4. Camera Adapter Module:
   • Computes frame-wise Plücker embeddings $P_i \in \mathbb{R}^{6 \times H \times W}$ from camera extrinsics (per-pixel ray origins and directions), forming a trajectory tensor $\mathbf{P}$.
   • Adapts these embeddings via a dedicated network (PixelUnshuffle, Conv2d, residual stack) and fuses them into the latent denoising trajectory, allowing per-frame explicit camera control.
5. Time-Aware Dynamic Window Range Strategy:
   • Maintains global temporal consistency using a dynamically shifted, overlapping sliding window for video denoising during long-sequence inference.
   • Algorithmically adapts window boundaries and overlaps to ensure smooth transitions and coverage across all frames, with minimum window length enforcement at sequence edges.

Module	Input Features	Primary Output
Audio Semantics	Waveform, beat/bar seg., Qwen	Embeddings, T/E semantics
MV-Director	User goal, audio, prompt	Structured shot list
DiT Generator	Shots, audio, image, camera	Per-shot video clip
Camera Adapter	Trajectory parameters	Camera-embedded diffusion
TDW Strategy	Schedules, audio embedding	Overlapping windowed clips

3. Multi-Stage Generation Pipeline

The end-to-end workflow is structured into three primary stages:

• Stage 1: Shot Planning — MV-Director processes music audio, semantic descriptors, and user-supplied goals to generate a sequence of shot definitions, each with temporal boundaries, prompts, and camera behaviors suitable for the music structure.
• Stage 2: Clip-Wise Generation — Each shot interval is initialized with random latent noise $z_T^{[s_i:e_i]}$ and synthesized via inverse diffusion using DiT, with cross-attention conditioning on text, image, audio, and camera trajectory embeddings.
• Stage 3: Temporal Fusion and Editing — Concatenates the sequence of generated video clips, aligns overlapping frames (by cross-fading or blending), and optionally applies subtitle overlays and style corrections for global video coherence.

The output is a contiguous, long-duration music video with synchronized performance, motion, and cinematography reflective of the input music and user intent (Chen et al., 2 Dec 2025).

4. Learning Objectives and Optimization

YingVideo-MV employs the following loss functions and optimization mechanisms:

• Flow Matching Loss (camera trajectory denoising):

$$L_{\rm mse} = \mathbb{E}_{z_0, z_1, t} \left\| v_t - u_\theta(z_t, t, T, I, V, A, \mathcal C) \right\|_2^2, \quad v_t = z_1 - z_0$$

where $z_t = t z_0 + (1-t) z_1$; ensures accurate camera transition modeling.

• Diffusion Reconstruction Loss: Standard denoising score matching,

$$L_{\rm diff} = \mathbb{E}_{x, \epsilon, t} \left\| \epsilon - \epsilon_\theta(z_t, t \mid {\rm cond}) \right\|_2^2$$

optimizing video latent recovery.

• Direct Preference Optimization (DPO) Loss:

$$L_{\rm DPO} = -\mathbb{E}_{(x, y^w, y^l)} \Big[\log\sigma\big(\beta\log\frac{\pi_\theta(y^w\mid x)}{\pi_{\rm ref}(y^w\mid x)} - \beta\log\frac{\pi_\theta(y^l\mid x)}{\pi_{\rm ref}(y^l\mid x)}\big)\Big]$$

using segment-wise rankings derived from reward models (Sync-C, VideoReward, Hand-Specific) to optimize generation preference.

• Flow-DPO Refinement: Co-regularizes flow fields to align with high-reward denoising trajectories.
• Regularization:
  • LoRA L2 penalty on low-rank adaptation matrices.
  • Implicit CLIP-based appearance loss via cross-attention.

This suite of losses enables explicit guidance of audiovisual-content, identity, camera, and quality rewards during training.

5. Dataset Construction and Benchmarks

YingVideo-MV utilizes a purpose-built Music-in-the-Wild Dataset comprising two phases:

• Stage 1 dataset: Approximately 1,500 hours of solo performer videos (face and body-focused, average 10s clips) with broad musical and stylistic diversity.
• Stage 2 MV subset: 400 hours of professionally produced music performance videos with reliable audio-visual alignment and high cinematic quality.
• Preprocessing includes face/body detection, multimodal alignment verification, and quality filtering.

Benchmark datasets for evaluation include HDTF, CelebV-HQ, EMTD (talking-heads focus), and MultiCamVideo (camera motion), providing metrics for both generation quality and cinematographic fidelity (Chen et al., 2 Dec 2025).

6. Experimental Evaluation

Comprehensive experiments assess YingVideo-MV along fine-grained dimensions:

• Quantitative metrics:
  • Frame quality: FID = 30.36 (second best)
  • Temporal consistency: FVD = 193.68 (second best)
  • Lip sync: Sync-C = 6.07 (highest), Sync-D = 8.67 (lowest)
  • Identity preservation: CSIM = 0.753 (highest)
  • Camera accuracy: TransErr = 4.85 (lowest), RotErr = 1.22
• Baselines:
  • Surpasses InfiniteTalk, StableAvatar, CameraCtrl, Uni3C in multi-modal, long-video, and camera control metrics.
• User studies:
  • Mean scores: camera smoothness (4.3±0.6), lip-sync (4.5±0.5), movement naturalness (4.2±0.5), overall quality (4.4±0.6), all outperforming alternatives.
• Ablations:
  • Removing DPO raises FID/FVD and impairs synchronization/consistency (e.g., FID=35.02, FVD=203.71, CSIM=0.728).
  • Excluding the time-aware dynamic window (TDW) increases FVD by 6.3% and reduces synchronization metrics.

This establishes YingVideo-MV as a state-of-the-art approach for music-driven video generation, particularly in scenarios requiring explicit cross-modal, temporal, and cinematographic coordination (Chen et al., 2 Dec 2025).

7. Future Directions and Limitations

YingVideo-MV advances the field by unifying explicit shot planning, spatiotemporal diffusion, and camera-controllable synthesis. Identified limitations and avenues for future research include:

• Non-human subject synthesis: Generation involving fantastical or non-human avatars is not addressed and may require hierarchical, reference-aware architectures.
• Multi-character music videos: Extension to scenarios with multiple interacting agents (MC-MV) necessitates new models for spatial reasoning and synchronized group action.
• Generalization beyond performance videos: A plausible implication is that extending the model to free-form generative video or dialog-driven content may require rethinking the alignment and planning modules.

YingVideo-MV represents a benchmark framework for cross-modal music video generation, setting a foundation for the next generation of controllable, semantically aligned, and cinematically expressive video synthesis models (Chen et al., 2 Dec 2025).