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This paper introduces MultiRef-Compass, a comprehensive benchmark designed for evaluating multi-reference-to-audio-video (MR2AV) generation, addressing the limitations of existing benchmarks that primarily focus on single-reference or text-driven generation. The benchmark includes 350 curated samples and an evaluation protocol that assesses four key dimensions: Basic Quality, Reference Consistency, Audio-Visual Consistency, and Instruction Following, utilizing 14 sub-metrics for a robust analysis. Experimental results across eight MR2AV systems highlight significant gaps in performance, emphasizing the necessity for a structured framework to advance research in this emerging area.
MultiRef-Compass reveals that current MR2AV systems have substantial performance gaps, highlighting the urgent need for a standardized evaluation framework in this novel domain.
Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mainly focus on text-driven generation, single-reference subject preservation, or isolated audio-video alignment, leaving the emerging MR2AV setting largely unexplored. Compared with these settings, MR2AV requires models to jointly reason over multiple references while generating synchronized visual and audio content. Models must not only preserve each reference faithfully but also correctly bind and compose multiple referenced entities into coherent audio-visual events. To address this gap, we introduce MultiRef-Compass, a unified benchmark for MR2AV generation. It comprises $350$ carefully curated samples constructed through a scalable and controllable asset-composition pipeline, covering multi-view subject preservation, multi-entity binding, and human-object-scene composition. To provide interpretable assessment, MultiRef-Compass defines an evaluation protocol with four dimensions: Basic Quality, Reference Consistency, Audio-Visual Consistency, and Instruction Following, using 14 sub-metrics. MultiRef-Compass integrates automatic metrics with a rejudging-enhanced MLLM-as-a-Judge framework, enabling scalable and auditable evaluation of both perceptual fidelity and reference-conditioned composition. Extensive experiments on eight representative MR2AV systems reveal substantial room for improvement across multiple evaluation dimensions, underscoring the need for a comprehensive benchmark and positioning MultiRef-Compass as a foundation for future MR2AV research.
