Abstract Motion tracking has been an important technique for imitating human‐like movement from large‐scale datasets in physics‐based motion synthesis. However, existing approaches focus on tracking either single character or a particular type of interaction, limiting their ability to handle contact‐rich interactions. Extending single‐character tracking approaches suffers from the instability due to the challenge of forces transferred through contacts. Contact‐rich interactions requires levels of control, which places much greater demands on model capacity. To this end, we propose a robust tracking method based on progressive neural network (PNN) where multiple experts are specialized in learning skills of various difficulties. Our method learns to assign training samples to experts automatically without requiring manually scheduling. Both qualitative and quantitative results show that our method delivers more stable motion tracking in densely interactive movements while enabling more efficient model training.

The integration of artificial intelligence (AI) into the new product development (NPD) process has been shown to significantly enhance innovation success. Drawing on situated AI theory, this study investigates two core senior management behaviours—emphasis on AI adoption and reward systems for AI adoption – and their roles in catalysing AI-driven innovation and facilitating AI integration across the seven NPD stages. Based on empirical analysis of 558 AI-driven NPD projects, the findings reveal that both senior management’s emphasis on AI adoption and reward systems positively impact innovation performance. Notably, senior management’s emphasis on AI adoption, rather than reward systems, significantly improves innovation quality, innovation speed, and product innovativeness. The results further indicate that senior management’s emphasis on AI adoption facilitates AI integration across six of the seven NPD stages, with the exception of the product testing stage. In contrast, reward systems for AI adoption positively influence AI usage in four stages, primarily within the early (idea development, business analysis, product design) and late (operations management) phases of the NPD process. This research advances situated AI theory to elucidate the mechanisms through which senior management behaviours drive AI-driven innovation success and adoption, and guides manager in effectively leveraging AI’s potential within the NPD process.

Motion in-betweening is the problem to synthesize movement between keyposes. Traditional research focused primarily on single characters. Extending them to densely interacting characters is highly challenging, as it demands precise spatial-temporal correspondence between the characters to maintain the interaction, while creating natural transitions towards predefined keyposes. In this research, we present a method for long-horizon interaction in-betweening that enables two characters to engage and respond to one another naturally. To effectively represent and synthesize interactions, we propose a novel solution called Cross-Space In-Betweening, which models the interactions of each character across different conditioning representation spaces. We further observe that the significantly increased constraints in interacting characters heavily limit the solution space, leading to degraded motion quality and diminished interaction over time. To enable long-horizon synthesis, we present two solutions to maintain long-term interaction and motion quality, thereby keeping synthesis in the stable region of the solution space. We first sustain interaction quality by identifying periodic interaction patterns through adversarial learning. We further maintain the motion quality by learning to refine the drifted latent space and prevent pose error accumulation. We demonstrate that our approach produces realistic, controllable, and long-horizon in-between motions of two characters with dynamic boxing and dancing actions across multiple keyposes, supported by extensive quantitative evaluations and user studies.

Abstract We propose a real‐time method for reactive motion synthesis based on the known trajectory of input character, predicting instant reactions using only historical, user‐controlled motions. Our method handles the uncertainty of future movements by introducing an intention predictor, which forecasts key joint intentions to make pose prediction more deterministic from the historical interaction. The intention is later encoded into the latent space of its reactive motion, matched with a codebook which represents mappings between input and output. It samples from the categorical distribution for pose generation and strengthens model robustness through adversarial training. Unlike previous offline approaches, the system can recursively generate intentions and reactive motions using feedback from earlier steps, enabling real‐time, long‐term realistic interactive synthesis. Both quantitative and qualitative experiments show our approach outperforms other matching‐based motion synthesis approaches, delivering superior stability and generalisability. In our method, user can also actively influence the outcome by controlling the moving directions, creating a personalised interaction path that deviates from predefined trajectories.