Creating Realistic Character Animations with AI Networks
This patent describes how to generate lifelike digital character animations using artificial intelligence networks, specifically by predicting joint movements and overall body motion from either random inputs or specific key poses.
Original patent title: “Generating realistic animations for digital animation characters utilizing a generative adversarial network and a hip motion prediction network”
This patent describes how to generate lifelike digital character animations using artificial intelligence networks, specifically by predicting joint movements and overall body motion from either random inputs or specific key poses. Granted to Adobe in 2021 with 23 claims and 1 forward citation, and it is expected to expire in 2039.
Coverage
What does this patent actually cover?
The patent details a system for generating digital character animations. It starts by identifying a "code vector" (ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 1), which can be a random input for new animations or derived from "keyframes" (Claim 2) that define specific poses. A "generative adversarial network" (GAN) then uses this code vector to create a "sequence of local poses" (Claim 1), which are the positions of a character's joints relative to its main "root joint" (like the hip). Next, a "root joint motion prediction network" calculates "hip joint velocities" and "global poses" (Claim 1), which means it figures out how the character's entire body moves in the animation space. Finally, the system generates the full animation based on these global poses. For example, an animator could provide a few key poses for a character, and the system would automatically fill in the realistic movements between them.
The gap
What does this patent NOT cover?
- Does not cover animation generation without using a generative adversarial network (GAN) to produce local poses.
- Does not cover systems that predict global body motion without specifically determining hip joint velocities from local poses.
- Does not cover animation methods that rely solely on traditional manual keyframe interpolation without AI networks.
- Does not cover generating animations where the root joint motion prediction network is not a convolutional neural network, as specified in ClaimclaimA numbered sentence at the end of a patent that legally defines what the inventor owns. The most important section.Read more → 3.
- Does not cover systems that generate animations without first identifying a code vector or a set of keyframes as input.
These exclusions are unique to PatentBrief — derived from the actual claim language, not patent-office boilerplate.
Key facts
What made this novel
The noveltynoveltyThe requirement that an invention be different from anything publicly known before its priority date.Read more → lies in combining a generative adversarial network to create detailed local joint movements with a separate root joint motion prediction network that specifically uses hip velocities to generate realistic global body motion, ensuring the character moves naturally within the scene.
The Patent Drawing
Schematic visualization of the patent's claim structure. Hand-drawn diagrams in progress for each landmark patent.
Where you've seen this
Real-world examples
Adobe Character Animator
Video game character animation
Film and TV visual effects
Virtual reality avatars
Motion capture data cleanup and enhancement
Why it matters
The bigger picture
This technology is important for making digital characters move more naturally and efficiently, reducing the manual effort for animators. It allows for the creation of diverse and realistic movements, which is crucial in video games, film, and virtual reality. Adobe, as the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, is a major player in creative software, and this patent reflects their investment in AI-driven content creation tools.
Filed
June 25, 2019
Granted
March 30, 2021
Market context
Who's building on this
Companies in this space
Adobe Inc., as the assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →, continues to develop and integrate AI-powered features into its creative suite, including tools like Character Animator and Fuse. Other major players in 3D animation software, such as Autodesk (Maya, MotionBuilder) and Epic Games (Unreal Engine), are also heavily investing in AI and machine learning for character animation, often incorporating similar techniques for procedural generation and motion synthesis.
Market impact
This patent contributes to a broader trend of automating and enhancing animation workflows, making high-quality character animation more accessible and less labor-intensive. It enables content creators to rapidly prototype animations or generate variations, potentially reducing production costs and accelerating content creation in film, games, and virtual reality. It helps cement Adobe's position in AI-driven creative tools.
Claim 1 — Plain English
What this patent covers
The patent details a system for generating digital character animations. It starts by identifying a "code vector" (Claim 1), which can be a random input for new animations or derived from "keyframes" (Claim 2) that define specific poses. A "generative adversarial network" (GAN) then uses this code vector to create a "sequence of local poses" (Claim 1), which are the positions of a character's joints relative to its main "root joint" (like the hip). Next, a "root joint motion prediction network" calculates "hip joint velocities" and "global poses" (Claim 1), which means it figures out how the character's entire body moves in the animation space. Finally, the system generates the full animation based on these global poses. For example, an animator could provide a few key poses for a character, and the system would automatically fill in the realistic movements between them.
The clever bit
The novelty lies in combining a generative adversarial network to create detailed local joint movements with a separate root joint motion prediction network that specifically uses hip velocities to generate realistic global body motion, ensuring the character moves naturally within the scene.
What it does not cover
- Does not cover animation generation without using a generative adversarial network (GAN) to produce local poses.
- Does not cover systems that predict global body motion without specifically determining hip joint velocities from local poses.
- Does not cover animation methods that rely solely on traditional manual keyframe interpolation without AI networks.
- Does not cover generating animations where the root joint motion prediction network is not a convolutional neural network, as specified in Claim 3.
- Does not cover systems that generate animations without first identifying a code vector or a set of keyframes as input.
Patent timeline
Application submitted to the patent office
Application published, typically 18 months after filing
Patent officially issued
Patent enters public domain
PatentBrief Score
Impact Score
Early stage
Citation count
6/40
Early citations
Claim breadth
15/20
Broad claimsclaimsThe numbered statements at the end of a patent that legally define what the inventor owns.Read more →
Recency
10/20
Granted 5–10 years ago
Assignee scale
0/20
Independent or smaller assigneeassigneeThe entity that owns the patent — usually the inventor's employer or a company.Read more →
PatentBrief Impact Score — based on citation count, claim breadth, recency, and assignee scale. Not a legal assessment.
Heuristic Value Estimate
What this patent might be worth
$62K – $200K
Midpoint $125K · 12.8 yr remaining · industry ×1.6
Heuristic only — blends forward/backward citation counts, claim scope, time remaining, litigation history, and CPC-derived industry baseline. Real valuations need a professional appraisal.
Claim text not yet imported for this patent
The original legal language
Original claims
23 claims as filed with the patent office.
Concepts involved
Citations
Patent lineage
Cite this patent
Barnes, C., Yang, J., Zhou, Y., & Lu, J. (2021). Creating Realistic Character Animations with AI Networks (U.S. Patent No. 10,964,084). U.S. Patent and Trademark Office. https://patentbrief.org/patent/us/10964084/generating-realistic-animations-for-digital-animation-characters-utilizing-a-gen
Auto-generated from the patent record. Double-check author order and the issue date against the official USPTO document before submitting.
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Common Questions
Frequently Asked Questions
What does Creating Realistic Character Animations with AI Networks cover?
This patent describes how to generate lifelike digital character animations using artificial intelligence networks, specifically by predicting joint movements and overall body motion from either random inputs or specific key poses.
Who owns patent US 10964084?
Adobe owns this patent, granted in 2021.
When does this patent expire?
This patent is expected to expire on June 25, 2039, when the invention enters the public domain.
What is patent US 10964084 cited by?
This patent has been cited by 1 later patents that build on its ideas.
What problem does this patent solve?
This technology is important for making digital characters move more naturally and efficiently, reducing the manual effort for animators. It allows for the creation of diverse and realistic movements, which is crucial in video games, film, and virtual reality. Adobe, as the assignee, is a major player in creative software, and this patent reflects their investment in AI-driven content creation tools.
What does this patent NOT cover?
Does not cover animation generation without using a generative adversarial network (GAN) to produce local poses.
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