Large and high-quality motion datasets are essential for advancing human motion modeling. However, limitations of existing motion datasets, such as insufficient scale or inadequate quality, significantly hinder the progress of this field. To address these limitations, we introduce Mimic-X, a large-scale (52 hours), physically plausible 3D human motion dataset. To construct Mimic-X, we develop an adaptive option framework that controls a physically simulated character to imitate low-quality motions extracted from a vast collection of online videos. Specifically, we first apply hierarchical clustering to group motions into clusters, and then train option policies to mimic motions sampled from these clusters. Considering the noisy nature of low-quality motions, we utilize a separate encoder for each cluster to map the noisy motions within the cluster into a compact latent space. This significantly enhances the quality of the imitated motions while accelerating the learning process. Subsequently, we employ dynamic programming as a meta-policy to efficiently organize the option policies to generate complete motion clips. Finally, we perform fine-tuning to each motion sequence to further refine motion quality. The proposed adaptive option framework outperforms state-of-the-art human motion recovery methods across various evaluation metrics, demonstrating that motions in Mimic-X exhibit higher quality and greater physical plausibility. Furthermore, experimental results show that Mimic-X enhances the performance of motion generation methods, verifying its effectiveness for motion modeling tasks.

Although part-based motion synthesis networks have been investigated to reduce the complexity of modeling heterogeneous human motions, their computational cost remains prohibitive in interactive applications. To this end, we propose a novel two-part transformer network that aims to achieve high-quality, controllable motion synthesis results in real-time. Our network separates the skeleton into the upper and lower body parts, reducing the expensive cross-part fusion operations, and models the motions of each part separately through two streams of auto-regressive modules formed by multi-head attention layers. However, such a design might not sufficiently capture the correlations between the parts. We thus intentionally let the two parts share the features of the root joint and design a consistency loss to penalize the difference in the estimated root features and motions by these two auto-regressive modules, significantly improving the quality of synthesized motions. After training on our motion dataset, our network can synthesize a wide range of heterogeneous motions, like cartwheels and twists. Experimental and user study results demonstrate that our network is superior to state-of-the-art human motion synthesis networks in the quality of generated motions.

Deep learning techniques have been employed to design a controllable human motion synthesizer. Despite their potential, however, designing a neural network-based motion synthesis that enables flexible user interaction, fine-grained controllability, and the support of new types of motions at reduced time and space consumption costs remains a challenge. In this paper, we propose a novel approach, a neural motion graph, that addresses the challenge by enabling scalability to new motions while using compact neural networks. Our approach represents each type of motion with a separate neural node to reduce the cost of adding new motion types. In addition, designing a separate neural node for each motion type enables task-specific control strategies and has greater potential to achieve a high-quality synthesis of complex motions, such as the Mongolian dance. Furthermore, a single transition network, which acts as neural edges, is used to model the transition between two motion nodes. The transition network is designed with a lightweight control module to achieve a fine-grained response to user control signals. Overall, the design choice makes the neural motion graph highly controllable and scalable. In addition to being fully flexible to user interaction through high-level and fine-grained user-control signals, our experimental and subjective evaluation results demonstrate that our proposed approach, neural motion graph, outperforms state-of-the-art human motion synthesis methods in terms of the quality of controlled motion generation.

Abstract Inner product arguments allow a prover to prove that the inner product of two committed vectors equals a public scalar. They are used to reduce the complexity of many cryptographic primitives, such as range proofs. Range proofs are deployed in numerous applications to prove that a committed value lies in a certain range. As core building blocks, their complexity largely determines the performance of corresponding applications. In this paper, we have optimised the inner product argument with statement including two vector commitments (IPA tvc ) and range proof of Daza et al. (PKC’20), the inner product argument with statement including only one vector commitment (IPA ovc ) of Bünz et al. (S&P′18). For IPA tvc , we reduce the concrete communication complexity by 2 log 2 n field elements, where n is the vector dimension. For range proofs, we reduce the concrete communication and prover complexities by about 2 log 2 m field elements and 11 m field multiplications, respectively, where m is the bit length of range. For IPA ovc , we exponentially reduce the asymptotic verifier complexity from linear to logarithmic. Due to the asymptotic characteristics, our protocols are highly competitive when the vector dimension or bit length of range is large.