ACT is a trajectory-conditioned framework for topology-general skeletal animation that injects 3D point trajectories from monocular video into skeletons via a Routed Trajectory Injector for improved fidelity and temporal consistency.
Charac- tershot: Controllable and consistent 4d character animation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
T2Mo generates controllable dynamic 3D shapes by conditioning on both text semantics and 3D trajectories with a shape-grounded embedding for arbitrary inputs.
A scalable pipeline generates an intra-consistent, inter-diverse 1.4M style image dataset from text-to-image models and uses it to train a style encoder and generalizable style transfer model.
citing papers explorer
-
Follow Your Track: Precise Skeleton Animation Controlled by 3D Trajectories
ACT is a trajectory-conditioned framework for topology-general skeletal animation that injects 3D point trajectories from monocular video into skeletons via a Routed Trajectory Injector for improved fidelity and temporal consistency.
-
Controllable Dynamic 3D Shape Generation via 3D Trajectories and Text
T2Mo generates controllable dynamic 3D shapes by conditioning on both text semantics and 3D trajectories with a shape-grounded embedding for arbitrary inputs.
-
MegaStyle: Constructing Diverse and Scalable Style Dataset via Consistent Text-to-Image Style Mapping
A scalable pipeline generates an intra-consistent, inter-diverse 1.4M style image dataset from text-to-image models and uses it to train a style encoder and generalizable style transfer model.