Computer Graphics Forum最新文献

Animation of hand-drawn sketches is an adorable art. It allows the animator to generate animations with expressive freedom and requires significant expertise. In this work, we introduce a novel sketch animation framework designed to address inherent challenges, such as motion extraction, motion transfer, and occlusion. The framework takes an exemplar video input featuring a moving object and utilizes a robust motion transfer technique to animate the input sketch. We show comparative evaluations that demonstrate the superior performance of our method over existing sketch animation techniques. Notably, our approach exhibits a higher level of user accessibility in contrast to conventional sketch-based animation systems, positioning it as a promising contributor to the field of sketch animation. https://graphics-research-group.github.io/SketchAnim/

Learning-based methods for 3D content generation have shown great potential to create 3D characters from text prompts, videos, and images. However, current methods primarily focus on generating static 3D meshes, overlooking the crucial aspect of creating an animatable 3D meshes. Directly using 3D meshes generated by existing methods to create underlying skeletons for animation presents many challenges because the generated mesh might exhibit geometry artifacts or assume arbitrary poses that complicate the subsequent rigging process. This work proposes a new framework for generating a 3D animatable mesh from a single 2D image depicting the character. We do so by enforcing the generated 3D mesh to assume an A-pose, which can mitigate the geometry artifacts and facilitate the use of existing automatic rigging methods. Our approach aims to leverage the generative power of existing models across modalities without the need for new data or large-scale training. We evaluate the effectiveness of our framework with qualitative results, as well as ablation studies and quantitative comparisons with existing 3D mesh generation models.

In multiplayer, first-person shooter games like Counter-Strike: Global Offensive (CS:GO), coordinated movement is a critical component of high-level strategic play. However, the complexity of team coordination and the variety of conditions present in popular game maps make it impractical to author hand-crafted movement policies for every scenario. We show that it is possible to take a data-driven approach to creating human-like movement controllers for CS:GO. We curate a team movement dataset comprising 123 hours of professional game play traces, and use this dataset to train a transformer-based movement model that generates human-like team movement for all players in a “Retakes” round of the game. Importantly, the movement prediction model is efficient. Performing inference for all players takes less than 0.5 ms per game step (amortized cost) on a single CPU core, making it plausible for use in commercial games today. Human evaluators assess that our model behaves more like humans than both commercially-available bots and procedural movement controllers scripted by experts (16% to 59% higher by TrueSkill rating of “human-like”). Using experiments involving in-game bot vs. bot self-play, we demonstrate that our model performs simple forms of teamwork, makes fewer common movement mistakes, and yields movement distributions, player lifetimes, and kill locations similar to those observed in professional CS:GO match play.

Animating gaze behavior is crucial for creating believable virtual characters, providing insights into their perception and interaction with the environment. In this paper, we present an efficient yet natural-looking gaze animation model applicable to real-time walking characters exploring natural environments. We address the challenge of dynamic gaze adaptation by combining findings from neuroscience with a data-driven saliency model. Specifically, our model determines gaze focus by considering the character's locomotion, environment stimuli, and terrain conditions. Our model is compatible with both automatic navigation through pre-defined character trajectories and user-guided interactive locomotion, and can be configured according to the desired degree of visual exploration of the environment. Our perceptual evaluation shows that our solution significantly improves the state-of-the-art saliency-based gaze animation with respect to the character's apparent awareness of the environment, the naturalness of the motion, and the elements to which it pays attention.

Recent progress in physics-based character control has made it possible to learn policies from unstructured motion data. However, it remains challenging to train a single control policy that works with diverse and unseen motions, and can be deployed to real-world physical robots. In this paper, we propose a two-stage technique that enables the control of a character with a full-body kinematic motion reference, with a focus on imitation accuracy. In a first stage, we extract a latent space encoding by training a variational autoencoder, taking short windows of motion from unstructured data as input. We then use the embedding from the time-varying latent code to train a conditional policy in a second stage, providing a mapping from kinematic input to dynamics-aware output. By keeping the two stages separate, we benefit from self-supervised methods to get better latent codes and explicit imitation rewards to avoid mode collapse. We demonstrate the efficiency and robustness of our method in simulation, with unseen user-specified motions, and on a bipedal robot, where we bring dynamic motions to the real world.

Generating high-fidelity garment animations through traditional workflows, from modeling to rendering, is both tedious and expensive. These workflows often require repetitive steps in response to updates in character motion, rendering viewpoint changes, or appearance edits. Although recent neural rendering offers an efficient solution for computationally intensive processes, it struggles with rendering complex garment animations containing fine wrinkle details and realistic garment-and-body occlusions, while maintaining structural consistency across frames and dense view rendering. In this paper, we propose a novel approach to directly synthesize garment animations from body motion sequences without the need for an explicit garment proxy. Our approach infers garment dynamic features from body motion, providing a preliminary overview of garment structure. Simultaneously, we capture detailed features from synthesized reference images of the garment's front and back, generated by a pre-trained image model. These features are then used to construct a neural radiance field that renders the garment animation video. Additionally, our technique enables garment recoloring by decomposing its visual elements. We demonstrate the generalizability of our method across unseen body motions and camera views, ensuring detailed structural consistency. Furthermore, we showcase its applicability to color editing on both real and synthetic garment data. Compared to existing neural rendering techniques, our method exhibits qualitative and quantitative improvements in garment dynamics and wrinkle detail modeling. Code is available at https://github.com/wrk226/GarmentAnimationNeRF.

Motion in-betweening has emerged as a promising approach to enhance the efficiency of motion creation due to its flexibility and time performance. However, previous in-betweening methods are limited to generating short transitions due to growing pose ambiguity when the number of missing frames increases. This length-related constraint makes the optimization hard and it further causes another constraint on the target pose, limiting the degrees of freedom for artists to use. In this paper, we introduce a keyframe-driven approach that effectively solves the pose ambiguity problem, allowing robust in-betweening performance on various lengths of missing frames. To incorporate keyframe-driven motion synthesis, we introduce a keyframe score that measures the likelihood of a frame being used as a keyframe as well as an adaptive keyframe selection method that maintains appropriate temporal distances between resulting keyframes. Additionally, we employ phase manifolds to further resolve the pose ambiguity and incorporate trajectory conditions to guide the approximate movement of the character. Comprehensive evaluations, encompassing both quantitative and qualitative analyses, were conducted to compare our method with state-of-the-art in-betweening approaches across various transition lengths. The code for the paper is available at https://github.com/seokhyeonhong/long-mib

Physics-based character motions remain difficult to create and control. We make two contributions towards simpler specification and faster generation of physics-based control. First, we introduce a novel partwise model predictive control (MPC) method that exploits independent planning for body parts when this proves beneficial, while defaulting to whole-body motion planning when that proves to be more effective. Second, we introduce a new approach to motion specification, based on specifying an ordered set of contact keyframes. These each specify a small number of pairwise contacts between the body and the environment, and serve as loose specifications of motion strategies. Unlike regular keyframes or traditional trajectory optimization constraints, they are heavily under-constrained and have flexible timing. We demonstrate a range of challenging contact-rich motions that can be generated online at interactive rates using this framework. We further show the generalization capabilities of the method.