Generating motion sequences conforming to a target style while adhering to the given content prompts requires accommodating both the content and style. In existing methods, the information usually only flows from style to content, which may cause conflict between the style and content, harming the integration. Differently, in this work we build a bidirectional control flow between the style and the content, also adjusting the style towards the content, in which case the style-content collision is alleviated and the dynamics of the style is better preserved in the integration. Moreover, we extend the stylized motion generation from one modality, i.e. the style motion, to multiple modalities including texts and images through contrastive learning, leading to flexible style control on the motion generation. To further boost the performance, we advance the motion diffusion to motion-aligned temporal latent diffusion by developing a novel motion VAE. Extensive experiments demonstrate that our method significantly outperforms previous methods across different datasets, while also enabling multimodal signals control. The code of our method will be made publicly available.

Language plays a vital role in the realm of human motion. Existing methods have largely depended on CLIP text embeddings for motion generation, yet they fall short in effectively aligning language and motion due to CLIP’s pretraining on static image-text pairs. This work introduces LaMP, a novel Language-Motion Pretraining model, which transitions from a language-vision to a more suitable language-motion latent space. It addresses key limitations by generating motion-informative text embeddings, significantly enhancing the relevance and semantics of generated motion sequences. With LaMP, we advance three key tasks: text-to-motion generation, motion-text retrieval, and motion captioning through aligned language-motion representation learning. For generation, LaMP instead of CLIP provides the text condition, and an autoregressive masked prediction is designed to achieve mask modeling without rank collapse in transformers. For retrieval, motion features from LaMP’s motion transformer interact with query tokens to retrieve text features from the text transformer, and vice versa. For captioning, we finetune a large language model with the language-informative motion features to develop a strong motion captioning model. In addition, we introduce the LaMP-BertScore metric to assess the alignment of generated motions with textual descriptions. Extensive experimental results on multiple datasets demonstrate substantial improvements over previous methods across all three tasks. Project page: https://aigc3d.github.io/LaMP

Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors. Previous methods usually quantize the entire body pose into one code, which not only faces the difficulty in encoding all joints within one vector but also loses the spatial relationship between different joints. Differently, in this work we quantize each individual joint into one vector, which i) simplifies the quantization process as the complexity associated with a single joint is markedly lower than that of the entire pose; ii) maintains a spatial-temporal structure that preserves both the spatial relationships among joints and the temporal movement patterns; iii) yields a 2D token map, which enables the application of various 2D operations widely used in 2D images. Grounded in the 2D motion quantization, we build a spatial-temporal modeling framework, where 2D joint VQVAE, temporal-spatial 2D masking technique, and spatial-temporal 2D attention are proposed to take advantage of spatial-temporal signals among the 2D tokens. Extensive experiments demonstrate that our method significantly outperforms previous methods across different datasets, with a $26.6\%$ decrease of FID on HumanML3D and a $29.9\%$ decrease on KIT-ML.

Many existing mobile robotic telepresence systems have equipped with two cameras, one is a forward-facing camera for video communication, and the other is a downward-facing camera for robot navigation. However, the two live videos from these two cameras would cause some confusion which makes it difficult for a remote operator to perceive the local environment. In this paper, we propose to use a user interface with one stitched live video instead of two live videos for mobile robotic telepresence systems. We used a video stitching algorithm to stitch the two live videos into one live video through which a remote operator can well perceive the local environment. We conducted a user study to investigate the difference between one stitched live video and two separate live videos in the user interface. The results show that the user interface with one stitched live video improves task efficiency, the number of errors, and remote operators’ feelings of presence, and enables remote operators to concentrate on the work they are doing.

Reconstructing the high-resolution volumetric 3D shape from images is challenging due to the cubic growth of computational cost. In this paper, we propose a Fourier-based method that reconstructs a 3D shape from images in a 2D space by predicting slices in the frequency domain. According to the Fourier slice projection theorem, we introduce a thickness map to bridge the domain gap between images in the spatial domain and slices in the frequency domain. The thickness map is the 2D spatial projection of the 3D shape, which is easily predicted from the input image by a general convolutional neural network. Each slice in the frequency domain is the Fourier transform of the corresponding thickness map. All slices constitute a 3D descriptor and the 3D shape is the inverse Fourier transform of the descriptor. Using slices in the frequency domain, our method can transfer the 3D shape reconstruction from the 3D space into the 2D space, which significantly reduces the computational cost. The experiment results on the ShapeNet dataset demonstrate that our method achieves competitive reconstruction accuracy and computational efficiency compared with the state-of-the-art reconstruction methods.

Discriminative dictionary learning (DDL) provides an appealing paradigm for appearance modeling in visual tracking. However, most existing DDL-based trackers cannot handle drastic appearance changes, especially for scenarios with background cluster and/or similar object interference. One reason is that they often suffer from the loss of subtle visual information, which is critical to distinguish an object from distracters. In this paper, we explore the use of activations from the convolutional layer of a convolutional neural network to improve the object representation and then propose a robust distracter-resistive tracker via learning a multi-component discriminative dictionary. The proposed method exploits both the intra-class and inter-class visual information to learn shared atoms and the class-specific atoms. By imposing several constraints into the objective function, the learned dictionary is reconstructive, compressive, and discriminative, and thus can better distinguish an object from the background. In addition, our convolutional features have structural information for object localization and balance the discriminative power and semantic information of the object. Tracking is carried out within a Bayesian inference framework where a joint decision measure is used to construct the observation model. To alleviate the drift problem, the reliable tracking results obtained online are accumulated to update the dictionary. Both the qualitative and quantitative results on the CVPR2013 benchmark, the VOT2015 data set, and the SPOT data set demonstrate that our tracker achieves substantially better overall performance against the state-of-the-art approaches.

Object representations are of great importance for robust visual tracking. Although the high‐dimensional representation can effectively encode the input data with more information, exploiting it in a real‐time tracking system would be intractable and infeasible due to the high computational cost and memory requirements. In this study, the authors propose a compact discriminative object representation to achieve both good tracking accuracy and efficiency. An ensemble of weak training sets is generated based on the self‐representative ability of tracking samples, which is applied to learn discriminative functions. Each candidate is represented by the concatenation of project values on all the weak training sets. Tracking is then carried out within a Bayesian inference framework where the classification score of the support vector machine is used to construct the observation model. The evaluations on TB50 benchmark dataset demonstrate that the proposed algorithm is much more computationally efficient than the state‐of‐the‐art methods with comparable accuracy.