Learning‐based pose edition for efficient and interactive design

@article{Victor2021LearningbasedPE,
  title={Learning‐based pose edition for efficient and interactive design},
  author={L'eon Victor and Alexandre Meyer and Sa{\"i}da Bouakaz},
  journal={Computer Animation and Virtual Worlds},
  year={2021},
  volume={32}
}
Authoring an appealing animation for a virtual character is a challenging task. In computer‐aided keyframe animation artists define the key poses of a character by manipulating its underlying skeletons. To look plausible, a character pose must respect many ill‐defined constraints, and so the resulting realism greatly depends on the animator's skill and knowledge. Animation software provide tools to help in this matter, relying on various algorithms to automatically enforce some of these… 

References

SHOWING 1-10 OF 20 REFERENCES

Intuitive Facial Animation Editing Based On A Generative RNN Framework

This paper designs a generative recurrent neural network that generates realistic motion into designated segments of an existing facial animation, optionally following user‐provided guiding constraints, and demonstrates the usability of the system on several animation editing use cases.

Natural Character Posing from a Large Motion Database

An interactive inverse kinematics approach that robustly generates natural poses in a large human-reachable space and can relieve animators from time-consuming, back-and-forth, IK-pose adjustment, NAT-IK overcomes limitations.

Spatial Motion Doodles: Sketching Animation in VR Using Hand Gestures and Laban Motion Analysis

A method for easily drafting expressive character animation by playing with instrumented rigid objects and capturing the expressiveness of user-manipulation by analyzing Laban effort qualities in the input spatial motion doodles and transferring them to the synthetic motions the authors generate.

Learned motion matching

This work combines the benefits of both approaches and, by breaking down the Motion Matching algorithm into its individual steps, shows how learned, scalable alternatives can be used to replace each operation in turn.

Neural state machine for character-scene interactions

The proposed Neural State Machine, a novel data-driven framework to guide characters to achieve goal-driven actions with precise scene interactions, and introduces a control scheme that combines egocentric inference and goal-centric inference.

Inverse Kinematics Techniques in Computer Graphics: A Survey

This survey presents a comprehensive review of the IK problem and the solutions developed over the years from the computer graphics point of view, and suggests which IK family of solvers is best suited for particular problems.

A deep learning framework for character motion synthesis and editing

A framework to synthesize character movements based on high level parameters, such that the produced movements respect the manifold of human motion, trained on a large motion capture dataset, can produce smooth, high quality motion sequences without any manual pre-processing of the training data.

FABRIK: A fast, iterative solver for the Inverse Kinematics problem

Phase-functioned neural networks for character control

A real-time character control mechanism using a novel neural network architecture called a Phase-Functioned Neural Network that takes as input user controls, the previous state of the character, the geometry of the scene, and automatically produces high quality motions that achieve the desired user control.

Extending FABRIK with model constraints

A human‐like model that has been structured hierarchically and sequentially using FABRIK is presented, utilising most of the suggested joint models; it can efficiently trace targets in real time, without oscillations or discontinuities, verifying the effectiveness of FABriK.