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Learn how to create expressive character performances with Act-Two, our most advanced motion capture yet. Now supporting hand and upper body tracking as well as an even wider variety of characters and styles.

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Runway

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22,972 görüntüleme • 11 ay önce •via X (Twitter)

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Introducing Act-Two, our next-generation motion capture model with major improvements in generation quality and support for head, face, body and hand tracking. Act-Two only requires a driving performance video and reference character. Available now to all our Enterprise customers and Creative Partners. Learn more about Act-Two below. 1/4
1:11

Introducing Act-Two, our next-generation motion capture model with major improvements in generation quality and support for head, face, body and hand tracking. Act-Two only requires a driving performance video and reference character. Available now to all our Enterprise customers and Creative Partners. Learn more about Act-Two below. 1/4

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Introducing, Act-One. A new way to generate expressive character performances inside Gen-3 Alpha using a single driving video and character image. No motion capture or rigging required. Learn more about Act-One below. (1/7)
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Introducing, Act-One. A new way to generate expressive character performances inside Gen-3 Alpha using a single driving video and character image. No motion capture or rigging required. Learn more about Act-One below. (1/7)

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Act-Two allows you to create highly expressive scenes entirely driven by the nuanced performances of your actors. The timing, delivery, body language and subtle expressions are all faithfully transposed from your driving performances to your generated characters. Learn more below. 1/3
2:02

Act-Two allows you to create highly expressive scenes entirely driven by the nuanced performances of your actors. The timing, delivery, body language and subtle expressions are all faithfully transposed from your driving performances to your generated characters. Learn more below. 1/3

Runway

24,226 görüntüleme • 11 ay önce

Generate expressive performances using a single driving video and character image with Act-One. Learn how in today's Runway Academy.
1:37

Generate expressive performances using a single driving video and character image with Act-One. Learn how in today's Runway Academy.

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The two bad things about supporting arbitrary 3d models as characters is that I have to actually learn how to use the unity animator, and character sprites interact incredibly poorly with other 3d geometry that gets really close to them.
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The two bad things about supporting arbitrary 3d models as characters is that I have to actually learn how to use the unity animator, and character sprites interact incredibly poorly with other 3d geometry that gets really close to them.

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At Unreal Fest Stockholm, Voice to Visuals brought together Unreal Engine’s most powerful animation tools, showing how expressive performances can come to life in real time. Using Take Recorder, Sequencer, MetaHuman Animator, Live Link Hub, and Performance Capture Manager — alongside Captury’s markerless motion capture — the demo showcased end-to-end performance capture and playback inside UE. From voice-driven lip sync to real-time retargeting across diverse characters, and even dynamic character swaps, this workflow demonstrates a new era of speed, creativity, and flexibility for animation and more!
1:21

At Unreal Fest Stockholm, Voice to Visuals brought together Unreal Engine’s most powerful animation tools, showing how expressive performances can come to life in real time. Using Take Recorder, Sequencer, MetaHuman Animator, Live Link Hub, and Performance Capture Manager — alongside Captury’s markerless motion capture — the demo showcased end-to-end performance capture and playback inside UE. From voice-driven lip sync to real-time retargeting across diverse characters, and even dynamic character swaps, this workflow demonstrates a new era of speed, creativity, and flexibility for animation and more!

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PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking paper page: introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. Toward the goal of naturalism, we animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos. We create combinatorial diversity by randomizing character appearance, motion profiles, materials, lighting, 3D assets, and atmospheric effects. Our dataset currently includes 104 videos, averaging 2,000 frames long, with orders of magnitude more correspondence annotations than prior work. We show that existing methods can be trained from scratch in our dataset and outperform the published variants. Finally, we introduce modifications to the PIPs point tracking method, greatly widening its temporal receptive field, which improves its performance on PointOdyssey as well as on two real-world benchmarks.
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PointOdyssey: A Large-Scale Synthetic Dataset for Long-Term Point Tracking paper page: introduce PointOdyssey, a large-scale synthetic dataset, and data generation framework, for the training and evaluation of long-term fine-grained tracking algorithms. Our goal is to advance the state-of-the-art by placing emphasis on long videos with naturalistic motion. Toward the goal of naturalism, we animate deformable characters using real-world motion capture data, we build 3D scenes to match the motion capture environments, and we render camera viewpoints using trajectories mined via structure-from-motion on real videos. We create combinatorial diversity by randomizing character appearance, motion profiles, materials, lighting, 3D assets, and atmospheric effects. Our dataset currently includes 104 videos, averaging 2,000 frames long, with orders of magnitude more correspondence annotations than prior work. We show that existing methods can be trained from scratch in our dataset and outperform the published variants. Finally, we introduce modifications to the PIPs point tracking method, greatly widening its temporal receptive field, which improves its performance on PointOdyssey as well as on two real-world benchmarks.

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What exactly is "Wuxia," and how does Phantom Blade Zero define it? In a recent interview in China, our Director Soulframe shares how we capture the spirit of wuxia, drawing from authentic kung fu and blending it with motion capture to create distinct fighting styles and layered narrative design. 👇

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How to CREATE Long AI Videos with Consistent Characters in (2026) Learn how to create long cinematic AI videos with consistent characters, environments, and motion. Learn how build reference assets, chain clips together with previous video generations, and structure prompts so the final result feels like one continuous film instead of disconnected AI shots. This information is for both the new and existing Youtube creators, who want to learn how to create good videos, with consistent characters. Watch to the end, to learn properly. Like, Share and Retweet. Follow BIG CHRIS

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The release of our next generation motion capture model, Act-Two. How to use it. And some great work from the community. Get caught up on what happened This Week with Runway.
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The release of our next generation motion capture model, Act-Two. How to use it. And some great work from the community. Get caught up on what happened This Week with Runway.

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Mobile-TeleVision follows our previous idea on upper-body and lower-body separation for control: 1⃣We train RL for lower-body locomotion and standing; 2⃣We use IK and retargeting directly for upper-body for high-precision and smooth manipulation. We avoid using RL for upper-body motion tracking. This is because fundamentally it is hard to avoid errors with RL tracking, and sim2real gap brings another gap. At this point, we still find using IK is more practical and precise.
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Mobile-TeleVision follows our previous idea on upper-body and lower-body separation for control: 1⃣We train RL for lower-body locomotion and standing; 2⃣We use IK and retargeting directly for upper-body for high-precision and smooth manipulation. We avoid using RL for upper-body motion tracking. This is because fundamentally it is hard to avoid errors with RL tracking, and sim2real gap brings another gap. At this point, we still find using IK is more practical and precise.

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Blender AI motion capture plugin, supporting real-time capture and video upload. It is 4.2GB in size with 1 billion parameters, and includes facial, finger, and body animation.
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Blender AI motion capture plugin, supporting real-time capture and video upload. It is 4.2GB in size with 1 billion parameters, and includes facial, finger, and body animation.

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