Pith. sign in

REVIEW 5 major objections 6 minor 25 references

Towards Video to Piano Music Generation with Chain-of-Perform Support Benchmarks

T0 review · 5 major / 6 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read The paper builds a ten-hour, five-view piano video benchmark for evaluating video-to-piano music generation, with Chain-of-Perform annotations that split reasoning from generation.

desk verdict CoP benchmark idea is plausible, but the paper is a placeholder: no usable data, no evaluation on its own test set, and a table that contradicts its own narrative. read the letter →

arxiv 2505.20038 v1 pith:7GUB5LXP submitted 2025-05-26 cs.SD cs.CVeess.AS

Chang Liu , Haomin Zhang , Shiyu Xia , Zihao Chen , Chaofan Ding , Xin Yue , Huizhe Chen , Xinhan Di This is my paper · ORCID
classification cs.SDcs.CVeess.AS
keywords video-to-pianogenerationChain-of-Performmultimodalbenchmarkvideo-to-audioevaluationMIDImetricstemporalalignmentpianoperformancedataset
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper argues that existing video-to-audio evaluation sets do not capture the precise semantic and temporal synchronization piano music requires. To close that gap, it introduces the CoP Benchmark Dataset: ten hours of five-view piano performance by two pianists, annotated with step-by-step Chain-of-Perform guidance that separates musical reasoning from audio generation. The benchmark evaluates models at both stages, with format and outcome accuracy for the reasoning stage and standard plus MIDI-aware metrics for the generation stage. The intended consequence is a shared, open testbed that lets video-to-audio and video-to-music systems be compared fairly on a specialized piano task.

What carries the argument

The carrying mechanism is the Chain-of-Perform annotation pipeline, a video-to-piano analogue of chain-of-thought prompting. It splits the task into a Thinking Stage, where a fine-tuned large language model produces a step-by-step blueprint (components such as pitch, velocity, sustain, and the camera views to consult), and a Generation Stage, where four levels of outputs are produced from coarse MIDI up to style-conditioned audio. A five-view camera rig (top, left, right, front, and pedal) provides the temporal and semantic visual evidence, and two metric suites do the measuring: form and outcome accuracy for the reasoning output, and fidelity, diversity, and alignment metrics (Inception Score, Fréchet Audio Distance, CLAP, AV-align, KL divergence, MIDI precision and recall, and Mean Opinion Score) for the generated audio.

What would settle it

Take a random sample of the released ten-hour dataset, have two independent piano experts annotate the same videos with Chain-of-Perform steps, and measure inter-annotator agreement on the thinking-stage labels. If agreement is low, or if the labels frequently disagree with the corresponding MIDI and audio, then the reported 100% reasoning accuracy and the alignment metrics rest on unreliable ground truth.

Watch

Extended reading notes

Core claim

The central claim is that video-to-piano music generation needs its own benchmark, and that the CoP Benchmark Dataset fills that need. The dataset comprises ten hours of piano playing recorded from five fixed camera views (top, left, right, front, and pedal) so that hand positions, keyboard visibility, and pedal use are all available as visual cues. Each video carries Chain-of-Perform annotations: a thinking stage in which a fine-tuned language model turns a query into a structured plan listing MIDI pitch, velocity, sustain, and required views, and a generation stage with four refinement levels from coarse MIDI to style-conditioned audio. The paper reports that fine-tuned reasoning models reach 100% format and outcome accuracy on the thinking stage, while generation-stage scores on standard video-to-audio models remain modest, indicating that the bottleneck is the audio synthesis itself.

Load-bearing premise

The load-bearing premise is that the expert-written Chain-of-Perform annotations are accurate enough to serve as ground truth for both thinking-stage and generation-stage scores, yet the paper gives no annotation protocol or inter-annotator agreement data.

Editorial extensions

If this is right

  • A shared public leaderboard lets any video-to-audio or video-to-music system be compared on identical piano videos, annotations, and metrics.
  • Reasoning-stage evaluation (form and outcome accuracy) isolates whether a system can plan the right MIDI components before it generates audio.
  • The four generation levels expose exactly where a model fails, from coarse pitch and timestamp to velocity, sustain, and style-conditioned audio.
  • Because the benchmark reports near-perfect thinking-stage scores but low generation-stage quality, it redirects research effort toward audio synthesis rather than planning.
  • The open release of data and protocols makes it possible to reproduce every table number and to add new models to the leaderboard.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same two-stage evaluation could be adapted to other instrument or Foley domains: any video-to-sound task with a recognizable planning phase could reuse the form and outcome split and the level-based generation ladder.
  • A between-pianist comparison on the released data would test whether the benchmark's alignment metrics are sensitive to playing style, or whether they mostly reward matching the five-view recording setup.
  • If the dataset is adopted as a standard testbed, the 100% thinking-stage scores suggest that the discriminative part of the benchmark saturates quickly, so the useful headroom will be in the generation-stage and MIDI metrics.
  • A useful next check is to measure per-step rather than aggregate agreement in the thinking stage, which would show whether some annotation steps are systematically easier to predict than others.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

5 major / 6 minor

Summary. The paper proposes a video-to-piano music generation benchmark called Chain-of-Perform (CoP). It describes a 10-hour, five-view video dataset recorded by two pianists, an expert-annotated, step-by-step reasoning/generation hierarchy in four levels, and an evaluation suite covering both a "thinking stage" (format and outcome accuracy of an LLM-based planner) and a "generation stage" (FD, KL, MIDI metrics, MOS). Initial experiments are reported on several V2A models, with thinking-stage scores of 100% after fine-tuning and generation metrics on VGGSound/AudioCaps. The paper claims the dataset, annotations, and protocols are fully open-sourced.

Significance. If the claimed dataset and evaluation protocol existed and were validated, they would address a real gap: current V2A/V2M benchmarks are not specialized for piano performance, where temporal and semantic alignment to visual cues is critical. The proposed four-level CoP decomposition is a potentially useful organizing idea. However, the manuscript does not demonstrate that the artifact exists in usable form: there are no dataset statistics, no annotation validation, no evaluation results computed on the proposed benchmark itself, and the open-sourcing claim is not checkable. The paper therefore does not currently deliver on its central claim, despite the merits of the underlying motivation.

major comments (5)
  1. [Section 3.1] The central claim of the paper is the release of a fully open-sourced benchmark, but Section 3.1 provides no dataset statistics (number of clips, duration per view, train/test/validation split), no annotation protocol, no inter-annotator agreement, and no release metadata (license, checksum, version). The statement that "the expert-provided annotations in each step guide the model" is asserted without evidence that the annotations are consistent or correct; the thinking-stage scores and generation-stage alignment metrics both rest on this unvalidated ground truth.
  2. [Section 3.2 and Table 1] The evaluation suite is described as assessing video-to-piano generation, but the generation metrics in Table 1 are computed on the VGGSound and AudioCaps test sets, not on the proposed CoP piano dataset. Consequently, no number in the paper demonstrates that the proposed benchmark works as claimed. The piano-specific metrics promised in Section 3.2 (MIDI precision/recall/accuracy, MOS) are absent from Table 1, even though Section 4 states they are "very low"; these numbers must be reported to support the benchmark's contribution.
  3. [Table 1] There is an internal inconsistency in Table 1: the zero-shot and finetuned rows for MMAudio-S-16kHz are identical across all five generation metrics (1.19, 0.04, 1.08, 0.01, 0.0246), while the narrative says training improved performance. In addition, all finetuned thinking scores are 100%, while the corresponding zero-shot scores are described as "very low" but are omitted from the table. The table as presented cannot be used to assess whether fine-tuning helped or whether the 100% scores are meaningful.
  4. [Section 3.2 (Thinking Metrics)] The thinking-stage evaluation appears circular: models are fine-tuned and then scored against the same CoP annotations used as training targets, yielding 100% scores after training versus low zero-shot scores. This is consistent with annotation overfitting rather than valid reasoning. The paper needs held-out annotations, human evaluation of reasoning quality, or a transfer experiment to a different annotation set to establish that the scores measure generalization.
  5. [Section 4] The paper explicitly states that experiments are incomplete: "We will continue our experiments on a variety of the state-of-the-art V2M models" and "We will further conduct experiments...". A benchmark paper should report the benchmark's own evaluation results on its own dataset, not promise them. This missing evidence is load-bearing for the claim that CoP is a usable, standardized benchmark.
minor comments (6)
  1. [Abstract] The abstract contains a typo: "do not to fully capture" should be "do not fully capture".
  2. [Section 3.1] The naming is inconsistent: the text uses "CoT-like (CoP)" and "chain-of-perform(CoP)" without defining the acronym consistently; please settle on one form.
  3. [Table 1] The column headers and caption are unclear: "Params Format Score" is ambiguous, "FDPANNs" and "FDstdPANNs" are not defined, and the caption "Initial V2M Evaluation Metrics Towards Thinking" does not describe the table contents.
  4. [Section 3.2] The bullet list names IS, CLIP score, FAD, AV-align, and CLAP score as part of the evaluation suite, but Table 1 reports only FD and KL variants; the authors should either include the remaining metrics or explain why they are omitted.
  5. [References] Reference [12] is malformed, beginning with "Qwen, :, An Yang," rather than the author list; please correct the entry.
  6. [Section 3.1] The claimed 10-hour dataset is described only by total duration; please provide a breakdown by view, by pianist, and by level, along with audio and video specifications (frame rate, resolution, synchronization procedure).

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the benchmark paper's claims are not derived from their own outputs; observed weaknesses are evidential gaps, not circular reductions.

full rationale

The paper is a benchmark proposal rather than a derivation chain, and no load-bearing step reduces to its own input by construction. The CoP guidance is explicitly grounded in Chain-of-Thought prompting via Wei et al. [20]; the authors' own Yingsound [2] appears only as one evaluated baseline in Table 1, not as a premise justifying CoP, so the self-citation is not load-bearing. The Thinking Stage 100% scores are reported after fine-tuning, but the paper never states that evaluation is performed on the same annotations used as training labels; 'Outcome Accuracy' is the ordinary definition of agreement with expert annotations, not a construction that forces a perfect score. The absence of a train/test split, annotation protocol, and dataset statistics is a reproducibility and validity gap, not a circularity. Likewise, computing generation-stage FD/KL numbers on VGGSound and AudioCaps rather than on the CoP piano set weakens the benchmark's own evaluation but does not make any result equivalent to its inputs. Table 1's identical zero-shot and finetuned rows for MMAudio-S-16kHz are an internal inconsistency or reporting error, not a demonstrated reduction. Under the rule that circularity findings require an exhibited equation-to-equation or fit-to-prediction reduction, no such step is present.

Assumptions & free parameters 0 free parameters · 4 assumptions · 1 invented entities

The paper contains no fitted numeric constants, but it relies on several unvalidated domain assumptions: the camera views contain sufficient information, the four-level decomposition is a valid breakdown, and the expert annotations can serve as ground truth. These are introduced in Sections 3.1 and 3.2 without independent evidence, and the CoP concept is an introduced artifact rather than an externally validated benchmark.

assumptions (4)
  • domain assumption Five fixed camera views with visible keyboard and pedal are sufficient to recover piano MIDI and audio.
    Section 3.1 makes this the primary collection constraint without an ablation or sensitivity analysis.
  • ad hoc to paper The four-level CoP decomposition is a valid and complete representation for video-to-piano generation.
    Section 3.1 introduces levels 1-4 as the benchmark's core structure with no evidence, ablation, or user study.
  • ad hoc to paper Expert CoP annotations are accurate and consistent ground truth.
    Section 3.1 cites expert-provided annotations; no production protocol or inter-annotator agreement is given.
  • domain assumption Standard video-to-audio metrics plus thinking scores capture synchronization quality for piano music.
    Section 3.2 asserts these metrics; no correlation with human perception or failure analysis is reported.
invented entities (1)
  • Chain-of-Perform (CoP) benchmark dataset
    purpose: Standardized step-by-step annotated benchmark and evaluation suite for video-to-piano generation
    Introduced by this paper; no external validation, no downloadable artifact inspected, and the leaderboard is not accessible from the manuscript.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Towards Video to Piano Music Generation with Chain-of-Perform Support Benchmarks." pith.science (2026). https://pith.science/paper/7GUB5LXP

@misc{pith2026250520038,
  author       = {Pith},
  title        = {Pith review of: Towards Video to Piano Music Generation with Chain-of-Perform Support Benchmarks},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/7GUB5LXP}},
  note         = {Machine review of arXiv:2505.20038}
}
read the original abstract

Generating high-quality piano audio from video requires precise synchronization between visual cues and musical output, ensuring accurate semantic and temporal alignment.However, existing evaluation datasets do not fully capture the intricate synchronization required for piano music generation. A comprehensive benchmark is essential for two primary reasons: (1) existing metrics fail to reflect the complexity of video-to-piano music interactions, and (2) a dedicated benchmark dataset can provide valuable insights to accelerate progress in high-quality piano music generation. To address these challenges, we introduce the CoP Benchmark Dataset-a fully open-sourced, multimodal benchmark designed specifically for video-guided piano music generation. The proposed Chain-of-Perform (CoP) benchmark offers several compelling features: (1) detailed multimodal annotations, enabling precise semantic and temporal alignment between video content and piano audio via step-by-step Chain-of-Perform guidance; (2) a versatile evaluation framework for rigorous assessment of both general-purpose and specialized video-to-piano generation tasks; and (3) full open-sourcing of the dataset, annotations, and evaluation protocols. The dataset is publicly available at https://github.com/acappemin/Video-to-Audio-and-Piano, with a continuously updated leaderboard to promote ongoing research in this domain.

Figures

Figures reproduced from arXiv: 2505.20038 by the authors.

Figure 1
Figure 1. Video to Piano Music Generation with Chain-of-Perform(CoP) Dataset. [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

25 extracted references · 11 canonical work pages

  1. [1]

    Video-guided foley sound generation with multimodal con- trols

    Ziyang Chen, Prem Seetharaman, Bryan Russell, Oriol Ni- eto, David Bourgin, Andrew Owens, and Justin Salamon. Video-guided foley sound generation with multimodal con- trols. arXiv preprint arXiv:2411.17698, 2024. 1, 2

    arXiv 2024

  2. [2]

    YingSound: Video-Guided Sound Effects Generation with Multi-modal Chain-of-Thought Controls

    Zihao Chen, Haomin Zhang, Xinhan Di, Haoyu Wang, Sizhe Shan, Junjie Zheng, Yunming Liang, Yihan Fan, Xinfa Zhu, Wenjie Tian, et al. Yingsound: Video-guided sound ef- fects generation with multi-modal chain-of-thought controls. arXiv preprint arXiv:2412.09168, 2024. 3

    work page Pith review arXiv 2024

  3. [3]

    Tam- ing multimodal joint training for high-quality video-to-audio synthesis

    Ho Kei Cheng, Masato Ishii, Akio Hayakawa, Takashi Shibuya, Alexander Schwing, and Yuki Mitsufuji. Tam- ing multimodal joint training for high-quality video-to-audio synthesis. arXiv preprint arXiv:2412.15322, 2024. 1, 2, 3

    arXiv 2024

  4. [4]

    Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning

    Daya Guo, Dejian Yang, Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning. arXiv preprint arXiv:2501.12948, 2025. 3

    arXiv 2025

  5. [5]

    Gans trained by a two time-scale update rule converge to a local nash equilib- rium

    Martin Heusel, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, and Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilib- rium. Advances in neural information processing systems , 30, 2017. 3

    2017

  6. [6]

    Taming visually guided sound generation

    Vladimir Iashin and Esa Rahtu. Taming visually guided sound generation. In British Machine Vision Conference (BMVC), 2021. 1, 3

    work page 2021

  7. [7]

    Sophia Koepke, Olivia Wiles, Yael Moses, and Andrew Zisserman

    A. Sophia Koepke, Olivia Wiles, Yael Moses, and Andrew Zisserman. Sight to sound: An end-to-end approach for vi- sual piano transcription. ICASSP 2020 - 2020 IEEE Interna- tional Conference on Acoustics, Speech and Signal Process- ing (ICASSP), pages 1838–1842, 2020. 1

    work page 2020

  8. [8]

    Crandall, and Christopher Raphael

    Jangwon Lee, Bardia Doosti, Yupeng Gu, David Cartledge, David J. Crandall, and Christopher Raphael. Observing pianist accuracy and form with computer vision. 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 1505–1513, 2019. 1

    work page 2019

Show all 25 references
  1. [9]

    Tri-ergon: Fine-grained video-to-audio generation with multi-modal conditions and lufs control

    Bingliang Li, Fengyu Yang, Yuxin Mao, Qingwen Ye, Hongkai Chen, and Yiran Zhong. Tri-ergon: Fine-grained video-to-audio generation with multi-modal conditions and lufs control. arXiv preprint arXiv:2412.20378, 2024. 1

    2024 arXiv

  2. [10]

    Diff-foley: Synchronized video-to-audio synthesis with la- tent diffusion models, 2023

    Simian Luo, Chuanhao Yan, Chenxu Hu, and Hang Zhao. Diff-foley: Synchronized video-to-audio synthesis with la- tent diffusion models, 2023. 1

    2023

  3. [11]

    Foleygen: Visually-guided audio generation

    Xinhao Mei, Varun Nagaraja, Gael Le Lan, Zhaoheng Ni, Ernie Chang, Yangyang Shi, and Vikas Chandra. Foleygen: Visually-guided audio generation. In 2024 IEEE 34th Inter- national Workshop on Machine Learning for Signal Process- ing (MLSP), pages 1–6. IEEE, 2024. 1

    2024

  4. [12]

    Qwen2.5 technical report, 2025

    Qwen, :, An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Jun- yang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang...

    2025

  5. [13]

    Direct preference optimization: Your language model is secretly a reward model

    Rafael Rafailov, Archit Sharma, Eric Mitchell, Christo- pher D Manning, Stefano Ermon, and Chelsea Finn. Direct preference optimization: Your language model is secretly a reward model. Advances in Neural Information Processing Systems, 36, 2024. 1

    2024

  6. [14]

    Improved techniques for training gans

    Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016. 3

    2016

  7. [15]

    Audeo: Au- dio generation for a silent performance video

    Kun Su, Xiulong Liu, and Eli Shlizerman. Audeo: Au- dio generation for a silent performance video. ArXiv, abs/2006.14348, 2020. 1

    2006 arXiv

  8. [16]

    Audiox: Diffusion transformer for anything-to-audio generation

    Zeyue Tian, Yizhu Jin, Zhaoyang Liu, Ruibin Yuan, Xu Tan, Qifeng Chen, Wei Xue, and Yike Guo. Audiox: Diffusion transformer for anything-to-audio generation. arXiv preprint arXiv:2503.10522, 2025. 1

    2025 arXiv

  9. [17]

    Temporally aligned audio for video with autoregression

    Ilpo Viertola, Vladimir Iashin, and Esa Rahtu. Temporally aligned audio for video with autoregression. arXiv preprint arXiv:2409.13689, 2024. 1

    2024 arXiv

  10. [18]

    V2a-mapper: A lightweight solution for vision-to-audio generation by connecting foun- dation models, 2023

    Heng Wang, Jianbo Ma, Santiago Pascual, Richard Cartwright, and Weidong Cai. V2a-mapper: A lightweight solution for vision-to-audio generation by connecting foun- dation models, 2023. 1

    2023

  11. [19]

    Frieren: Efficient video-to-audio generation with rectified flow matching

    Yongqi Wang, Wenxiang Guo, Rongjie Huang, Jiawei Huang, Zehan Wang, Fuming You, Ruiqi Li, and Zhou Zhao. Frieren: Efficient video-to-audio generation with rectified flow matching. arXiv preprint arXiv:2406.00320, 2024. 1

    2024 arXiv

  12. [20]

    Chain-of-thought prompting elicits reasoning in large lan- guage models

    Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al. Chain-of-thought prompting elicits reasoning in large lan- guage models. Advances in neural information processing systems, 35:24824–24837, 2022. 2

    2022

  13. [21]

    Large-scale con- trastive language-audio pretraining with feature fusion and keyword-to-caption augmentation

    Yusong Wu, Ke Chen, Tianyu Zhang, Yuchen Hui, Taylor Berg-Kirkpatrick, and Shlomo Dubnov. Large-scale con- trastive language-audio pretraining with feature fusion and keyword-to-caption augmentation. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Si...

    2023

  14. [22]

    Llava-o1: Let vision language models reason step- by-step

    Guowei Xu, Peng Jin, Li Hao, Yibing Song, Lichao Sun, and Li Yuan. Llava-o1: Let vision language models reason step- by-step. arXiv preprint arXiv:2411.10440, 2024. 1

    2024 arXiv

  15. [23]

    Diverse and aligned audio-to-video genera- tion via text-to-video model adaptation

    Guy Yariv, Itai Gat, Sagie Benaim, Lior Wolf, Idan Schwartz, and Yossi Adi. Diverse and aligned audio-to-video genera- tion via text-to-video model adaptation. In Proceedings of the AAAI Conference on Artificial Intelligence, pages 6639– 6647, 2024. 3

    2024

  16. [24]

    Improve vision language model chain-of- thought reasoning

    Ruohong Zhang, Bowen Zhang, Yanghao Li, Haotian Zhang, Zhiqing Sun, Zhe Gan, Yinfei Yang, Ruoming Pang, and Yiming Yang. Improve vision language model chain-of- thought reasoning. arXiv preprint arXiv:2410.16198, 2024. 1

    2024 arXiv

  17. [25]

    Foley- crafter: Bring silent videos to life with lifelike and synchro- nized sounds

    Yiming Zhang, Yicheng Gu, Yanhong Zeng, Zhening Xing, Yuancheng Wang, Zhizheng Wu, and Kai Chen. Foley- crafter: Bring silent videos to life with lifelike and synchro- nized sounds. arXiv preprint arXiv:2407.01494 , 2024. 1, 3

    2024 arXiv

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.