The growing demand for diverse and realistic character animations in video games and films has driven the development of natural language-controlled motion generation systems. While recent advances in text-driven 3D human motion synthesis have made significant progress, generating realistic multi-person interactions remains a major challenge. Existing methods, such as denoising diffusion models and autoregressive frameworks, have explored interaction dynamics using attention mechanisms and causal modeling. However, they consistently overlook a critical physical constraint: the explicit spatial distance between interacting body parts, which is essential for producing semantically accurate and physically plausible interactions. To address this limitation, we propose InterDist, a novel masked generative Transformer model operating in a discrete state space. Our key idea is to decompose two-person motion into three components: two independent, interaction-agnostic single-person motion sequences and a separate interaction distance sequence. This formulation enables direct learning of both individual motion and dynamic spatial relationships from text prompts. We implement this via a VQ-VAE that jointly encodes independent motions and relative distances into discrete codebooks, followed by a bidirectional masked generative Transformer that models their joint distribution conditioned on text. To better align motion and language, we also introduce a cross-modal interaction module to enhance text-motion association. Our approach ensures the generated motions exhibit both semantic alignment with textual descriptions and preserving plausible inter-character distances, setting a new benchmark for text-driven multi-person interaction generation.

Abstract Simulating wet clothing remains a significant challenge due to the complex physical interactions between moist fabric and the human body, compounded by the lack of dedicated datasets for training data‐driven models. Existing self‐supervised approaches struggle to capture moisture‐induced dynamics such as skin adhesion, anisotropic surface resistance, and non‐linear wrinkling, leading to limited accuracy and efficiency. To address this, we present SHGS, a novel self‐supervised framework for humidity‐controllable clothing simulation grounded in the physical modeling of capillary bridges that form between fabric and skin. We abstract the forces induced by wetness into two physically motivated components: a normal adhesive force derived from Laplace pressure and a tangential shear‐resistance force that opposes relative motion along the fabric surface. By formulating these forces as potential energy for conservative effects and as mechanical work for non‐conservative effects, we construct a physics‐consistent wetness loss. This enables self‐supervised training without requiring labeled data of wet clothing. Our humidity‐sensitive dynamics are driven by a multi‐layer graph neural network, which facilitates a smooth and physically realistic transition between different moisture levels. This architecture decouples the garment's dynamics in wet and dry states through a local weight interpolation mechanism, adjusting the fabric's behavior in response to varying humidity conditions. Experiments demonstrate that SHGS outperforms existing methods in both visual fidelity and computational efficiency, marking a significant advancement in realistic wet‐cloth simulation.

Motion retargeting is an active research area in computer graphics and animation, allowing for the transfer of motion from one character to another, thereby creating diverse animated character data. While this technology has numerous applications in animation, games, and movies, current methods often produce unnatural or semantically inconsistent motion when applied to characters with different shapes or joint counts. This is primarily due to a lack of consideration for the geometric and spatial relationships between the body parts of the source and target characters. To tackle this challenge, we introduce a novel spatially-preserving Skinned Motion Retargeting Network (SMRNet) capable of handling motion retargeting for characters with varying shapes and skeletal structures while maintaining semantic consistency. By learning a hybrid representation of the character's skeleton and shape in a rest pose, SMRNet transfers the rotation and root joint position of the source character's motion to the target character through embedded rest pose feature alignment. Additionally, it incorporates a differentiable loss function to further preserve the spatial consistency of body parts between the source and target. Comprehensive quantitative and qualitative evaluations demonstrate the superiority of our approach over existing alternatives, particularly in preserving spatial relationships more effectively.

Coloring line art images based on the colors of reference images is a crucial stage in animation production, which is time-consuming and tedious. This paper proposes a deep architecture to automatically color line art videos with the same color style as the given reference images. Our framework consists of a color transform network and a temporal refinement network based on 3U-net. The color transform network takes the target line art images as well as the line art and color images of the reference images as input and generates corresponding target color images. To cope with the large differences between each target line art image and the reference color images, we propose a distance attention layer that utilizes non-local similarity matching to determine the region correspondences between the target image and the reference images and transforms the local color information from the references to the target. To ensure global color style consistency, we further incorporate Adaptive Instance Normalization (AdaIN) with the transformation parameters obtained from a multiple-layer AdaIN that describes the global color style of the references extracted by an embedder network. The temporal refinement network learns spatiotemporal features through 3D convolutions to ensure the temporal color consistency of the results. Our model can achieve even better coloring results by fine-tuning the parameters with only a small number of samples when dealing with an animation of a new style. To evaluate our method, we build a line art coloring dataset. Experiments show that our method achieves the best performance on line art video coloring compared to the current state-of-the-art methods.

In the animation industry, the colorization of raw sketch images is a vitally important but very time-consuming task. This article focuses on providing a novel solution that semiautomatically colorizes a set of images using a single colorized reference image. Our method is able to provide coherent colors for regions that have similar semantics to those in the reference image. An active-learning-based framework is used to match local regions, followed by mixed-integer quadratic programming (MIQP) which considers the spatial contexts to further refine the matching results. We efficiently utilize user interactions to achieve high accuracy in the final colorized images. Experiments show that our method outperforms the current state-of-the-art deep learning based colorization method in terms of color coherency with the reference image. The region matching framework could potentially be applied to other applications, such as color transfer.

Image colorization is a classic and important topic in computer graphics, where the aim is to add color to a monochromatic input image to produce a colorful result. In this survey, we present the history of colorization research in chronological order and summarize popular algorithms in this field. Early work on colorization mostly focused on developing techniques to improve the colorization quality. In the last few years, researchers have considered more possibilities such as combining colorization with NLP (natural language processing) and focused more on industrial applications. To better control the color, various types of color control are designed, such as providing reference images or color-scribbles. We have created a taxonomy of the colorization methods according to the input type, divided into grayscale, sketch-based and hybrid. The pros and cons are discussed for each algorithm, and they are compared according to their main characteristics. Finally, we discuss how deep learning, and in particular Generative Adversarial Networks (GANs), has changed this field.

Abstract 3D animation production for storytelling requires essential manual processes of virtual scene composition, character creation, and motion editing, etc. Although professional artists can favorably create 3D animations using software, it remains a complex and challenging task for novice users to handle and learn such tools for content creation. In this paper, we present Write‐An‐Animation, a 3D animation system that allows novice users to create, edit, preview, and render animations, all through text editing. Based on the input texts describing virtual scenes and human motions in natural languages, our system first parses the texts as semantic scene graphs, then retrieves 3D object models for virtual scene composition and motion clips for character animation. Character motion is synthesized with the combination of generative locomotions using neural state machine as well as template action motions retrieved from the dataset. Moreover, to make the virtual scene layout compatible with character motion, we propose an iterative scene layout and character motion optimization algorithm that jointly considers character‐object collision and interaction. We demonstrate the effectiveness of our system with customized texts and public film scripts. Experimental results indicate that our system can generate satisfactory animations from texts.