Creating high-quality character animation remains an intricate and cumbersome process that requires skill, training, and craftsmanship to master. Recently, diffusion models have unlocked the ability to generate diverse movements from high-level condition signals such as text. For artist-friendly control, motion diffusion leveraging Bézier curves have been shown to allow precise joint-level conditioning. Yet, these works have been limited to joints at a fixed temporal stride, while animators require more temporal flexibility when keyframing or manipulating tangents to achieve animation principles such as easing in & out. In this work, we introduce a new Implicit Bézier Motion Model (IBMM), which during training is exposed to all possible configurations of control points, enabling control at arbitrary timings. This allows both precise and sparse joint-level control, anywhere in time and for any joint. In addition, we introduce a new quantitative measure of ease-in and -out, which leads to a novel condition over the motion generation process to reflect this artistic principle.

Animation is a challenging and time-consuming process where animators must manipulate hundreds of controls over space and time to create compelling motions. Recent advances in motion diffusion models have shown impressive results for general motion generation and hold the potential to reduce the number of controls manipulated by animators to achieve high quality results. However, these models are limited by their inability to match sparse constraints precisely, preventing frame-level joint control required by artists. Additionally, recent models are trained for specific characters, preventing reuse, and are incompatible for characters with only a small datasets available. To tackle these shortcomings, we propose a novel factorization of motion between a character-agnostic Bézier Motion Model (BMM), which can be trained on a large motion dataset, followed by a character-specific posing model, trainable on a much smaller pose dataset, that enables reuse across many characters. BMM provides accuracy for meeting sparse joint-level constraints by working in a reduced space of Bézier curves that better aligns the condition signal with the prediction space of our model. Additionally, the Bézier curves offer animators an intuitive interface compatible with existing authoring software. Through quantitative and qualitative comparisons, we show the effectiveness of our factorization and parametric subspace, enabling user control with higher fidelity.

Authoring 3D motions is a laborious process that requires manipulating and coordinating many control handles over time. Neural motion representations learned from large motion datasets have recently shown impressive capabilities in many motion completion tasks. However, current methods are not designed for interactive motion authoring workflows. The reasons being their requirement of a dense context of full poses, which takes considerable time to author, as well as their lack of joint-level controls for refinement. In this paper, we introduce a Neural Motion Rig called SKEL-Betweener, tailored to interactive motion authoring. SKEL-Betweener is able to generate long motion sequences from two poses only, and enables intermediate motion authoring via neural motion curves---intuitive joint-level controls for positions and orientations. Through user evaluations, we demonstrate the effectiveness of our Neural Motion Rig for efficiently creating and editing motions.

Posing a 3D character for film or game is an iterative and laborious process where many control handles (e.g. joints) need to be manipulated to achieve a compelling result. Neural Inverse Kinematics (IK) is a new type of IK that enables sparse control over a 3D character pose, and leverages full body correlations to complete the un-manipulated joints of the body. While neural IK is promising, current methods are not designed to preserve previous edits in posing workflows. Current models generate a single pose from the handles only—regardless of what was there previously—making it difficult to preserve any variations and hindering tasks such as pose and motion editing.