Generating human motion in complex 3D scenes from text is a challenging task with broad applications. However, existing methods often overlook realistic physical contact, resulting in visually plausible but physically unrealistic motion, e.g., penetration. To alleviate this, we propose IntentMotion, a novel framework that generates human motion in 3D scenes from natural language instructions by explicitly modeling intent. We first introduce the Intention-Guided Contact Field (IGCF). This differentiable voxel-based contact region representation explicitly aligns parsed language roles with spatial contact regions through a hierarchical attention mechanism. IGCF is jointly trained with a diffusion-based motion generator, allowing contact predictions to adapt dynamically through gradient feedback. To improve the controllability and physics-aware motion, we further propose an Intention-Aware Diffusion Model (IADM), which decouples the high-level semantic planning from the low-level contact refinement in a coarse-to-fine process. The optimized contact cues are utilized to guide the synthesis of a coarse trajectory, followed by refining detailed pose sequences under IGCF supervision. Experiments on the HUMANISE and LINGO datasets demonstrate that our IntentMotion outperforms recent baselines in contact accuracy, semantic alignment, and generalization to unseen scenes.

Motion style transfer is a significant research area in computer vision, enabling the rapid switching of stylistic variations for the same motion in virtual digital humans. This dramatically enhances the richness and realism of motions, making it widely applicable in multimedia contexts such as film, gaming, and the Metaverse. However, most existing methods employ a two-stream structure, which often overlooks the intrinsic relationships between content and style motions, resulting in information loss and misalignment. Additionally, these methods struggle to capture temporal dependencies in long-range motion sequences, resulting in less natural outputs. To address these limitations, we propose a Unified Motion Style Diffusion (UMSD) Framework that simultaneously extracts features from content and style motions, achieving comprehensive information interaction. We also introduce the Motion Style Mamba (MSM) denoiser, which, for the first time in motion style transfer, leverages Mamba's powerful sequence modelling capability to produce more temporally coherent stylized motion sequences. Furthermore, we design a diffusion-based content consistency loss and a style consistency loss to ensure that the framework preserves content motion while effectively learning style motion features. Extensive experiments demonstrate that our approach outperforms State-Of-The-Art (SOTA) methods qualitatively and quantitatively, achieving more realistic and coherent motion style transfer.

Computer animation's quest to bridge content and style has historically been a challenging venture, with previous efforts often leaning toward one at the expense of the other. This paper tackles the inherent challenge of content-style duality, ensuring a harmonious fusion where the core narrative of the content is both preserved and elevated through stylistic enhancements. We propose a novel Multi-condition Motion Latent Diffusion Model (MCM-LDM) for Arbitrary Motion Style Transfer (AMST). Our MCM-LDM significantly emphasizes preserving trajectories, recognizing their fundamental role in defining the essence and fluidity of motion content. Our MCM-LDM's cornerstone lies in its ability first to disentangle and then intricately weave together motion's tripartite components: motion trajectory, motion content, and motion style. The critical insight of MCM-LDM is to embed multiple conditions with distinct priorities. The content channel serves as the primary flow, guiding the overall structure and movement, while the trajectory and style channels act as auxiliary components and synchronize with the primary one dynamically. This mechanism ensures that multi-conditions can seamlessly integrate into the main flow, enhancing the overall animation without overshadowing the core content. Empirical evaluations underscore the model's proficiency in achieving fluid and authentic motion style transfers, setting a new benchmark in the realm of computer animation. The source code and model are available at https://github.com/XingliangJin/MCM-LDM.git.

Recognizing dangerous actions is a critical task in computer vision, especially for surveillance applications. While existing deep learning methods have been successful in confined environments, they struggle with the anomalous and salient variations of human postures in dangerous actions. Additionally, finer-grained dangerous actions require more discriminative cues, adding to the complexity of the task. To address these challenges, we propose a novel solution that models the intrinsic and invariant properties of dangerous actions at multiple temporal semantic levels. Concretely, we propose a Dual Temporal Transformers (DTT) to capture temporal interactions between distinct key points in the human body aggregation from shallow to deep layers, increasing the perception field from local to global, simultaneously. By doing so, our method avoids overfitting to unrelated or minor clues in videos and achieves a generalized representation of abnormal actions. We evaluate our approach on indoor and outdoor environments and found that DTT outperforms existing methods in terms of efficiency and accuracy. Our code and dataset are pubic available on https://github.com/AveryJohnsonJJ/DTT.git.

We present FineStyle, a novel framework for motion style transfer that generates expressive human animations with specific styles for virtual reality and vision fields. It incorporates semantic awareness, which improves motion representation and allows for precise and stylish animation generation. Existing methods for motion style transfer have all failed to consider the semantic meaning behind the motion, resulting in limited controls over the generated human animations. To improve, FineStyle introduces a new cross-modality fusion module called Dual Interactive-Flow Fusion (DIFF). As the first attempt, DIFF integrates motion style features and semantic flows, producing semantic-aware style codes for fine-grained motion style transfer. FineStyle uses an innovative two-stage semantic guidance approach that leverages semantic clues to enhance the discriminative power of both semantic and style features. At an early stage, a semantic-guided encoder introduces distinct semantic clues into the style flow. Then, at a fine stage, both flows are further fused interactively, selecting the matched and critical clues from both flows. Extensive experiments demonstrate that FineStyle outperforms state-of-the-art methods in visual quality and controllability. By considering the semantic meaning behind motion style patterns, FineStyle allows for more precise control over motion styles. Source code and model are available on https://github.com/XingliangJin/Fine-Style.git.