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版本更新 · v2arXiv:2606.28593

Animation2Code: Evaluating Temporal Visual Reasoning in Video-to-Code Generation

Anya Ji, Abhijith Varma Mudunuri, David M. Chan, Alane Suhr

原文摘要

While recent vision-language models (VLMs) have achieved significant improvements on static visual-to-code tasks such as generating code for webpages, charts, or SVGs, it remains unclear whether they can recover temporal dynamics when motion is present. To this end, we introduce Animation2Code, a benchmark for evaluating temporal visual reasoning via reconstructing executable web animation code from videos. Animation2Code consists of 1,069 web animation videos with diverse visual appearances and motion patterns, paired with corresponding HTML/CSS/JavaScript implementations. We propose two human-aligned metrics, appearance similarity and temporal similarity, which allow us to disentangle visual fidelity from temporal alignment when comparing rendered animations against ground-truth samples. Benchmarking state-of-the-art VLMs on this dataset shows that current VLMs struggle to maintain temporal consistency in reconstruction, even when achieving high appearance similarity, including under finetuning and iterative refinement settings. Code and data are available at https://anya-ji.github.io/animation2code-website .

cs.CV · cs.AI · cs.CL

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2605.26176

PitchBench: Measuring Pitch Hearing in Audio-Language Models

Milan Liessens Dujardin, Song-Ze Yu, Craver Corbyn Thomas-Smith, David M. Chan, Karina Nguyen

原文摘要

Audio-language models (ALMs) are increasingly used in real-world applications that require music understanding, including tutoring, captioning, recommendation, and production. Yet existing benchmarks rarely evaluate a fundamental component of this understanding: pitch hearing. Pitch underpins the identification of notes, chords, and melodies and the grounding of captions in musical events, but it remains unclear how reliably ALMs perceive fine-grained pitch across sound sources, acoustic conditions, and response formats. We introduce PitchBench, an evaluation suite comprising 28 tasks that assess absolute and relative pitch perception under variations in loudness, note duration, sound source, time stretching, background noise, and other acoustic conditions. Tasks range from identifying isolated pitches to tracking voices in four-part musical textures. Evaluating frontier ALMs, we find that pitch hearing remains highly unreliable. Models perform poorly across settings, with accuracy varying sharply depending on the notation or prompt format, indicating instruction drift. Specialized DSP and neural pitch baselines achieve mean scores of approximately 73.2% and 75.2%, respectively, compared with approximately 53.9% for the strongest ALM. We release PitchBench as an evaluation benchmark, a training dataset, and a Python package with data generation tools to support future research on pitch-aware audio-language modeling.

cs.SD · cs.AI

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2606.17055Berkeley BAIR

T-Rex: Tactile-Reactive Dexterous Manipulation

Dantong Niu, Zhuoyang Liu, Zekai Wang, Boning Shao, Zhao-Heng Yin, Anirudh Pai, Yuvan Sharma, Stefano Saravalle 等 34 位作者

原文摘要

The ability to react dynamically to tactile signals has long been considered crucial to agile human-level dexterity. Yet contemporary learning-based Vision-Language-Action (VLA) models for robotic manipulation generally either overlook the tactile modality or are limited to encoders with static cues, due in part to the scarcity of diverse training data and standardized evaluation, architectural constraints in current VLA models, and limitations of static tactile encoders. In this paper, we push the frontier of tactile-reactive manipulation by addressing all of these limitations. We propose a large-scale, 100-hour tactile-rich dataset collected via a novel, data-efficient recipe that prioritizes elementary motor primitives. To effectively exploit naturally high-frequency touch signals without sacrificing the existing capabilities of existing VLAs, we introduce a variable-rate Mixture-of-Transformers (MoT) architecture equipped with a novel temporal tactile VQ-VAE encoder. We demonstrate the effectiveness of tactile-reactive policies on 12 manipulation tasks requiring delicate force control and deformable object manipulation, achieving over 30% higher average success rate than the strongest baseline.

cs.RO

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arXiv ↗PDF ↗