Wenkai Cheng | Searcharxiv

arXiv2026

S2Accompanist: A Semantic-Aware and Structure-Guided Diffusion Model for Music Accompaniment Generation

High-fidelity text-to-music generation typically relies on massive proprietary datasets and immense computational resources. Existing models often struggle to generate coherent pure musical accompaniments and lack precise, localized semantic control due to their reliance on coarse, track-level annotations. To address these limitations under constrained data and computing resources, we propose S2Accompanist, a Semantic-Aware and Structure-Guided Diffusion Model developed for the ICME2026 ATTM Grand Challenge. Specifically, we design an automated data pipeline comprising structural segmentation, Large Audio-Language Model driven segment-level captioning, and dual-metric quality grading to overcome the absence of localized metadata in raw datasets. Furthermore, we propose a semantic-aware Variational Autoencoder fine-tuning strategy that explicitly distills foundational LeadSheet structures into the acoustic latent space, effectively improving the overall audio fidelity. Extensive experiments demonstrate that S2Accompanist achieves state-of-the-art objective performance on the ATTM Grand Challenge benchmark across both the Efficiency and Performance Tracks. With only 402M parameters, our model remains competitive compared to larger-scale unconstrained models and secured first place in the Efficiency Track.

eess.AS↗

arXiv2025

TiViBench: Benchmarking Think-in-Video Reasoning for Video Generative Models

The rapid evolution of video generative models has shifted their focus from producing visually plausible outputs to tackling tasks requiring physical plausibility and logical consistency. However, despite recent breakthroughs such as Veo 3's chain-of-frames reasoning, it remains unclear whether these models can exhibit reasoning capabilities similar to large language models (LLMs). Existing benchmarks predominantly evaluate visual fidelity and temporal coherence, failing to capture higher-order reasoning abilities. To bridge this gap, we propose TiViBench, a hierarchical benchmark specifically designed to evaluate the reasoning capabilities of image-to-video (I2V) generation models. TiViBench systematically assesses reasoning across four dimensions: i) Structural Reasoning & Search, ii) Spatial & Visual Pattern Reasoning, iii) Symbolic & Logical Reasoning, and iv) Action Planning & Task Execution, spanning 24 diverse task scenarios across 3 difficulty levels. Through extensive evaluations, we show that commercial models (e.g., Sora 2, Veo 3.1) demonstrate stronger reasoning potential, while open-source models reveal untapped potential that remains hindered by limited training scale and data diversity. To further unlock this potential, we introduce VideoTPO, a simple yet effective test-time strategy inspired by preference optimization. By performing LLM self-analysis on generated candidates to identify strengths and weaknesses, VideoTPO significantly enhances reasoning performance without requiring additional training, data, or reward models. Together, TiViBench and VideoTPO pave the way for evaluating and advancing reasoning in video generation models, setting a foundation for future research in this emerging field.

cs.CV↗