Authoring high-quality character animation is essential in multimedia production, especially for pre-rendered formats such as films and TV series. These workflows are often iterative, requiring frequent adjustments and immediate visual feedback, and typically involve editing existing motion, such as mocap data. We present Neural Motion Path (NMP), a deep learning-based system designed to support this authoring process by enabling full-body motion editing through joint-level motion path manipulation. While joint rotations are essential for expressive human motion, explicitly specifying them imposes a significant burden on users; NMP addresses this challenge by enabling intuitive position-only motion path editing while implicitly inferring plausible rotation trajectories. To address the inherent tension between motion plausibility and precise constraint satisfaction, NMP explicitly decouples motion synthesis from constraint enforcement within an autoregressive framework. NMP generates realistic, context-aware motion without fine-tuning every path detail, making it accessible to novices while supporting the demands of detailed animation authoring. It combines a motion generator with a novel RotationNet for inferring joint rotation, and a constraint imposer that enforces end-effector constraints via an Explicit-Weight Sparse Expert Model (EW-SEM). The system supports terrain adaptation and authoring operations like Concatenate, Insert, and Mix. Implemented as a Blender add-on, NMP supports real-time playback and interactive workflows. A user study with novice users shows that NMP improves satisfaction, efficiency, and perceived motion quality compared to the conventional layered keyframing approach, highlighting its potential as an accessible and effective authoring tool.

We present TouchWalker, a real-time system for controlling fullbody avatar locomotion using finger-walking gestures on a touchscreen. The system comprises two main components: TouchWalker-MotionNet, a neural motion generator that synthesizes full-body avatar motion on a per-frame basis from temporally sparse twofinger input, and TouchWalker-UI, a compact touch interface that interprets user touch input to avatar-relative foot positions. Unlike prior systems that rely on symbolic gesture triggers or predefined motion sequences, TouchWalker uses its neural component to generate continuous, context-aware full-body motion on a per-frame basis-including airborne phases such as running, even without input during mid-air steps-enabling more expressive and immediate interaction. To ensure accurate alignment between finger contacts and avatar motion, it employs a MoE-GRU architecture with a dedicated foot-alignment loss. We evaluate TouchWalker in a user study comparing it to a virtual joystick baseline with predefined motion across diverse locomotion tasks. Results show that TouchWalker improves users' sense of embodiment, enjoyment, and immersion.