Do motion retargeting and AI-generated motion preserve anticipatory visual information in boxing animation?

Limballe, A, Abdourahman Mahamoud, A, Guillaume, C, Bennett, S ORCID: 0000-0002-8673-0164, Richard, K and Franck, M (2026) Do motion retargeting and AI-generated motion preserve anticipatory visual information in boxing animation? In: SAP '26: Proceedings of the 2026 ACM Symposium on Applied Perception . pp. 1-12. (SAP '26: ACM Symposium on Applied Perception 2026, 26th - 27th, Rennes, France).

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Abstract

In combat sports simulation, expert users have extensive experience in extracting visual anticipatory information from their opponent’s movements, which should be preserved in virtual opponent animations. However, animations based on motion capture data rely on complex pipelines that may disrupt the visual information initially present in the real actor’s motion. Recent physics-based simulation (such as Adversarial Motion Prior, AMP) have recently been introduced to adapt character motion to interactive constraints while satisfying physical laws, but they may introduce additional distortions. In this paper, we examine how animating a virtual opponent using motion capture data (as a reference), retargeting techniques, and AMP influences the visual anticipatory performance of expert boxers. Ten boxers participated in a perceptual study using video clips with varying occlusion times, in which they were asked to predict the type of punch among four possible choices. The results show a clear decline in anticipation perceptual accuracy (PA) as occlusion occurs earlier. Hooks were more difficult to anticipate than straight punches (PA for rear hook = 0.455, lead hook = 0.435, rear straight = 0.660, lead straight = 0.744). Animations based on motion capture (mean accuracy = 0.758) led to significant better anticipation perceptual accuracy, for straight punches, compared to retargeting (mean accuracy = 0.700) and AMP (mean accuracy = 0.647). Overall, these results highlight the need to redesign traditional animation pipelines to better preserve the visual information picked up by expert users in virtual training simulators.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	08 Information and Computing Sciences; Computation Theory & Mathematics; 46 Information and computing sciences
Subjects:	R Medicine > RC Internal medicine > RC1200 Sports Medicine T Technology > T Technology (General)
Divisions:	Sport and Exercise Sciences
Publisher:	Association for Computing Machinery (ACM)
Date of acceptance:	6 July 2026
Date of first compliant Open Access:	26 August 2026
Date Deposited:	26 Aug 2026 08:32
Last Modified:	26 Aug 2026 08:32
DOI or ID number:	10.1145/3821409.3821464
URI:	https://researchonline.ljmu.ac.uk/id/eprint/29196

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