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Customized Condition Controllable Generation for Video Soundtrack
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Proposes C3GVS, a latent-diffusion framework that jointly generates music and sound effects for videos using contrastive visual-sound-music pretraining, mask-based cross-attention (SDMA), and score-guided noise optimization for customizable control.
Problem
Video soundtrack generation with two-stage pipelines fails to jointly model dynamic acoustic properties of music and sound effects, and lacks fine-grained user control during inference.
Approach
Maps video, music, and sound effect into a shared space via contrastive pretraining to condition an audio U-Net, injects music and sound effect conditions with a spectrum-divergence masked cross-attention, and steers the DDIM sampling with score-based guidance (CLAP emotion, pitch-video rhythm, style encoder, multi-condition) applied at each step.
Key results
- On FilmScoreDB, the model achieves music F1 of 68.36 versus 58.33 for the best prior baseline (AudioLDM-VA), and FD 3.42 versus 3.63 for Tango-VA.
- Sound-effect ImageBind score improves to 0.257 on FilmScoreDB versus 0.183 for Tango-VA, and on SymMV&HIMV to 0.185 versus 0.142 for SpecVQGAN.
- Subjective MOS on FilmScoreDB is 3.73 for the proposed model versus 2.12 for Tango-VA and 2.01 for AudioLDM-VA, with human reference at 4.48.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add fv2se to video-only conditioning (ablation) | C3GVS · FilmScoreDB | FAD: 4.59 -> 4.57 | Helped |
| Add fv2m to video-only conditioning (ablation) | C3GVS · FilmScoreDB | FAD: 4.59 -> 3.46 | Helped |
| Add both fv2m and fv2se visual representations (ablation) | C3GVS · FilmScoreDB | FAD: 3.46 -> 3.42 | Helped |
| Add fv2se when fv2m is absent (ablation) | C3GVS · FilmScoreDB | BCS: 60.15 -> 61.29 | Helped |
| Add fv2m when fv2se is absent (ablation) | C3GVS · FilmScoreDB | BCS: 60.15 -> 67.8 | Helped |
| Add fv2se to fv2m conditioning (ablation) | C3GVS · FilmScoreDB | BCS: 67.8 -> 68.71 | Helped |
| Remove the music mask Mm (ablation) | Spectrum Divergence Masked Attention · FilmScoreDB | FAD: 3.42 -> 3.49 | Hurt |
| Remove both sound-effect and music masks (ablation) | Spectrum Divergence Masked Attention · FilmScoreDB | FAD: 3.42 -> 3.43 | Hurt |
| Replace attention fusion with addition-based fusion (ablation) | C3GVS diffusion model · FilmScoreDB | FAD: 3.42 -> 4.18 | Hurt |
| Replace attention fusion with addition-based fusion (ablation) | C3GVS diffusion model · FilmScoreDB | BCS: 68.71 -> 50.11 | Hurt |
Surprising findings
- Masking only the sound-effect branch gave the highest ImageBind score, which the authors interpret as sound information being more redundant than music.
Limitations
- Limited to fixed-length soundtrack generation (10-second clips).
- Subjective evaluation relies on 23 participants and 60 pieces, a small scale.
- Dataset annotation quality and category diversity are limited, hindering conditional control.
Experimental setup
Datasets: FilmScoreDB SymMV HIMV HIMV40k
Benchmarks: FilmScoreDB SymMV&HIMV
Metrics: FD KL IS BCS DES COR F1 FAD ImageBind score MOS
Baselines: LORIS D2M-GAN CDCD DIFF-Foley Tango-VA AudioLDM-VA SpecVQGAN DITTO
Builds on: AudioLDM Freedom guidance DITTO CAVP Latent Diffusion Models
Closest papers by meaning
- V2Meow: Meowing to the Visual Beat via Video-to-Music Generation (AAAI 2024 · similarity 0.89)
- FilmComposer: LLM-Driven Music Production for Silent Film Clips (CVPR 2025 · similarity 0.88)
- SoundCTM: Unifying Score-based and Consistency Models for Full-band Text-to-Sound Generation (ICLR 2025 · similarity 0.88)
- Seeing and Hearing: Open-domain Visual-Audio Generation with Diffusion Latent Aligners (CVPR 2024 · similarity 0.87)
- VidMuse: A Simple Video-to-Music Generation Framework with Long-Short-Term Modeling (CVPR 2025 · similarity 0.87)
- Read, Watch and Scream! Sound Generation from Text and Video (AAAI 2025 · similarity 0.87)
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