Visual Representation Matters: Exploiting Temporal Differences in Video-to-Audio Generation

AI summary

Overview

Research area: Generative AI for cross-modal audio synthesis — specifically video-to-audio (V2A) generation with conditional latent diffusion models.

Technical level: Intermediate. The paper assumes familiarity with latent diffusion models, classifier-free guidance, and vision-language encoders such as CLIP, but its central idea (using frame differences as extra visual conditioning) is intuitive.

Scope: The authors propose TD-V2A, a framework that treats temporal differences between consecutive video frames as the key visual representation separating video-to-audio from image-to-audio generation, and show it improves generation quality on VGGSound and AudioSet benchmarks without extra supervision networks.

What This Paper Is About

Video-to-audio systems generate sound for silent video, and the only thing that truly separates them from image-to-audio systems is that video frames change over time. Most existing V2A methods try to exploit that change indirectly — by adding auxiliary audio-visual models, predicting intermediate audio structures, or calling in large multimodal language models — which adds complexity and strong assumptions. This paper instead asks whether the raw temporal differences (TD) between consecutive frames can themselves be turned into a better visual condition, and how best to train and sample with them.

Key Contributions

1. A framing of TD as the intrinsic distinction between V2A and I2A. The authors argue that if a video has no temporal differences across frames, it degenerates into a static image and V2A collapses to I2A, making temporal alignment meaningless. TD is therefore presented as the core cue, not a supplementary one.

2. A systematic comparison of TD at two representation levels. They define frame-level temporal differences (FTD), computed on raw pixels before visual encoding, and CLIP-level temporal differences (CTD), computed between consecutive CLIP embeddings. The investigation finds that preserving TD before visual encoding gives substantially more informative conditioning than modeling it in the compressed feature space.

3. Hierarchically Continual Learning (HCL). A three-stage training strategy that first initializes with large-scale text-to-audio pre-training, then adapts to V2A with raw visual representations, and finally jointly optimizes raw visual conditioning and TD-augmented conditioning.

4. Annealed Temporal Differences Guidance (ATDG). An inference-time guidance scheme that applies strong TD guidance during early sampling steps and gradually anneals it, so that raw visual (semantic) conditioning takes over as denoising moves toward fine detail.

Main Findings

• TD-V2A leads on most objective metrics on the VGGSound test set. With HCL, CLIP+FTD conditioning and ATDG, it reaches FAD 0.53, KL 2.16, IS 16.9, FD 3.79, IBS 33.8 and AA 89.1. For comparison, MMAudio reports FAD 0.81, KL 2.11, IS 11.9, FD 5.65 and IBS 28.0, and AudioX reports FAD 1.13, KL 2.57, IS 12.2, FD 8.83 and IBS 26.0.

• Temporal alignment accuracy is competitive with a method built to optimize it. Diff-Foley achieves AA 89.9 after pretraining contrastive models with an AA objective on VGGSound and AudioSet; TD-V2A reaches 89.1 and surpasses all other baselines on AA.

• Frame-level TD beats CLIP-level TD in ablations. Starting from T2A pre-training: pure CLIP gives FAD 0.64, KL 2.20, IS 16.6, FD 4.33, IBS 31.8, AA 87.2; CLIP+CTD gives 0.57, 2.20, 17.1, 3.93, 33.3, 88.3; CLIP+FTD gives 0.55, 2.18, 16.5, 3.98, 33.4, 88.8.

• FTD outperforms a dedicated contrastive audio-visual pretraining condition without its extra training. CLIP+CAVP yields FAD 0.57, KL 2.16, IS 16.4, FD 4.06, IBS 31.9, AA 88.5, described as requiring extra training objectives and stages.

• HCL adds further gains on top of FTD. Combining HCL with CLIP+FTD improves to FAD 0.53, KL 2.16, IS 16.9, FD 3.79, IBS 33.8, AA 89.1.

• Annealing the TD guidance weight matters. A low static setting (w_TD = 0.5) yields FAD 0.57, KL 2.18, IS 16.7, FD 3.85, IBS 33.6, AA 88.2 but weaker temporal synchronization; a high static setting (w_TD = 1.5) yields FAD 0.62, KL 2.21, IS 16.5, FD 4.01, IBS 33.0, AA 89.1 with noticeable distortion. ATDG balances both at 0.53, 2.16, 16.9, 3.79, 33.8, 89.1.

• A window length of k = 2 is the best trade-off. With k = 1 (too short for visual dynamics): FAD 0.58, KL 2.21, IS 17.2, FD 4.11, IBS 33.4, AA 88.6. With k = 3 (too large, sensitive to distraction): FAD 0.59, KL 2.17, IS 16.9, FD 3.94, IBS 33.8, AA 88.8. With k = 2: FAD 0.53, KL 2.16, IS 16.9, FD 3.79, IBS 33.8, AA 89.1.

• Subjective ratings are close to ground truth and above the compared baselines. Across 15 human raters on a 1–5 scale, TD-V2A scores OVL 3.68 ± 0.46, S-REL 3.85 ± 0.51 and T-REL 3.62 ± 0.48, versus ground truth at 3.81 ± 0.49, 3.81 ± 0.49 and 3.72 ± 0.47. Diff-Foley scores 2.46 ± 0.49, 2.59 ± 0.54 and 2.21 ± 0.45, while FoleyCrafter scores 3.17 ± 0.47, 3.23 ± 0.56 and 3.04 ± 0.50.

• The frame-level guidance scale has an optimum at 2.0. In Appendix B, varying w_f from 1.0 to 2.5 shows that moderate increases improve audio quality but exceeding the optimal value of 2.0 degrades fidelity.

Methodology in Plain English

The team starts from a standard conditional latent diffusion setup, where audio is compressed into a small latent space and a network learns to denoise it while being steered by a video condition. Their twist is what that condition contains.

First, they compute temporal differences by subtracting one frame from a later frame, with a small window k controlling how far apart the frames are. These difference frames are still images, so they can be fed into the same frozen pretrained CLIP encoder used for the raw frames. The resulting TD embedding is simply concatenated with the raw frame embedding, so no new encoders, motion networks, or auxiliary supervision are needed. They compare this frame-level version (FTD) against a version that differences CLIP embeddings instead (CTD).

Second, they train in three stages. Stage one pretrains a text-to-audio latent diffusion model using Stable Audio's DiT-based architecture with FLAN-T5 text conditioning, on a mixture of AudioCaps, AudioSet, VGGSound, FreeSound and MSD, for 2M iterations at batch size 64. Stage two swaps the text condition for CLIP embeddings of raw frames and fine-tunes on AudioSet and VGGSound for 0.3M iterations. Stage three jointly trains the model on raw-frame conditioning and TD-augmented conditioning, so the model learns what TD adds while preserving what it already knew.

Third, at sampling time they use a guidance formula that combines a constant frame-level guidance scale with a timestep-dependent TD scale that starts high and decays with an annealing exponent, so temporal cues dominate early coarse steps and semantic cues dominate later fine steps. They set the frame guidance scale to 2.0, the TD minimum to 0.5, the TD maximum to 1.5, and the annealing factor to 1.2, and use 67 sampling steps for ATDG versus 100 for pure CFG to keep the number of function evaluations equal.

Evaluation uses FAD, KL divergence, Inception Score, Fréchet Distance with a PANN tagging model, ImageBind Score, and Diff-Foley's temporal alignment accuracy, plus the human study described above. All audio is segmented into 10-second clips at 16 kHz, and the VGGSound test set contains about 15K 10-second clips.

Why This Matters

Impact on research. The paper reframes a design question in V2A: instead of asking which extra module to bolt on, it asks whether the input video already contains enough signal. Demonstrating that frame differencing beats a dedicated contrastive audio-visual pretraining condition suggests that several lines of auxiliary-supervision work could be simplified. It also connects V2A to the TD-based visual representation learning idea it cites as inspiration, extending temporal-difference principles from representation learning and reinforcement learning into cross-modal generation.

Real-world applications:

• Film and video post-production, where foley and sound effects must line up frame-accurately with on-screen action.
• Augmented and virtual reality, where ambient sound must respond to the user's or the scene's motion.
• Automatic dubbing and accessibility tools for silent or muted footage.
• Multimodal content creation, where creators need soundtracks generated from short clips without hand-labeling audio.

Industry relevance. Because TD-V2A requires no additional predictive networks, no auxiliary conditioning signals, and keeps the CLIP encoder frozen, it fits cleanly into existing latent-diffusion V2A pipelines with minimal architectural change. The reported gains in FAD, IS and IBS on public benchmarks, plus near-ground-truth subjective scores, make it an attractive drop-in upgrade for teams already working with DiT-style audio backbones.

Future Directions

• Scaling and backbone generality. The method is validated on a DiT backbone with a waveform-compressed latent space; it is not reported whether the same gains hold for other backbones or larger pre-training corpora.
• Choosing the temporal window adaptively. Window length is a fixed hyperparameter with k = 2 best among 1, 2 and 3; content-adaptive windows that vary with motion speed are an open question.
• Understanding when TD helps most. The qualitative comparison separates dynamic and static scenes, but the paper does not report a systematic per-scene-type breakdown of metrics, leaving open how much TD contributes when the video is nearly static.
• Interaction with text-conditioned V2A. The authors note that methods such as ThinkSound use extra text information and are not directly comparable to standard V2A, leaving the combination of TD conditioning with text guidance as unexplored in the main results.

Target Audience

Researchers and graduate students working on multimodal generation, diffusion models, or audio-visual learning will get the most from this paper, particularly those already familiar with V2A systems such as Diff-Foley, FoleyCrafter, MMAudio and AudioX. Practitioners building audio generation pipelines will find the method's low architectural overhead appealing, while readers new to the area will benefit from the paper's clear framing of why temporal information is the defining element of video-to-audio tasks.

Authors’ abstract

Video-to-audio (V2A) generation extends image-to-audio generation (I2A) by introducing consecutive frames that provide essential temporal cues for audio synthesis. However, existing conditional diffusion-based V2A methods typically enhance visual conditioning with additional audio-visual supervision, acoustic structure prediction, or reasoning from large multimodal models, requiring extra networks or strong inductive biases. Inspired by recent advances in visual representation learning, we introduce TD-V2A, which leverages temporal differences (TD) as the key representation that distinguishes V2A from I2A, enriching visual conditioning with minimal architectural modification. We first investigate TD at both the frame and feature levels to identify the most effective representation level at which TD complements visual representations. Based on these findings, we develop a hierarchically continual learning strategy and an annealed temporal differences guidance method to progressively learn and exploit TD information during diffusion training and sampling process, respectively. Extensive experiments on benchmark datasets demonstrate that effectively exploiting TD through our proposed framework significantly improves end-to-end V2A generation quality, even outperforming dedicated V2A representations such as contrastive audio-visual pretraining.