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Demonstrating 3D Motion Generation in Alchemist AI Alchemist AI’s 3D generation capabilities now extend to motion comprehension, delivering realistic, physics-driven animations for game design, simulations, and virtual environments. Dynamic Movement Frameworks • Humans: Models support essential animations such as walking, jumping, and object interaction. Inverse kinematics ensures natural limb...

20,018 次观看 • 1 年前 •via X (Twitter)

10 条评论

ALCHEMIST AI 🔮 的头像
ALCHEMIST AI 🔮1 年前

Built By: @3iside Download App:

Moescape AI 的头像
Moescape AI1 年前

Sign up & create wholesome anime art on Moescape AI now!

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Syrate1 年前

the fact that all of this is being done simply with prompts and zero coding is insane

Rex 的头像
Rex1 年前

$ALCH is always building despite price action. Better than most AI coins, yet it is undervalued. We will reach billions in due time.

@JAX7 的头像
@JAX71 年前

wen moon?

Ciph Xyz 的头像
Ciph Xyz1 年前

Nice! Looking forward to try the new physics features! I made this before the update

Cody Apple 🪨 的头像
Cody Apple 🪨1 年前

I am so wrong $ALCH is a tool not gaming specific $ALCH is da best brand

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Vortecia1 年前

Any plans for a UI/UX overhaul soon?

Shaikh Yasin 的头像
Shaikh Yasin1 年前

I got liquideted worst coin scam

Z 的头像
Z1 年前

It’s kinda insane how fast alch evolving $ALCH

相关视频

Multi-Track Timeline Control for Text-Driven 3D Human Motion Generation paper page: Recent advances in generative modeling have led to promising progress on synthesizing 3D human motion from text, with methods that can generate character animations from short prompts and specified durations. However, using a single text prompt as input lacks the fine-grained control needed by animators, such as composing multiple actions and defining precise durations for parts of the motion. To address this, we introduce the new problem of timeline control for text-driven motion synthesis, which provides an intuitive, yet fine-grained, input interface for users. Instead of a single prompt, users can specify a multi-track timeline of multiple prompts organized in temporal intervals that may overlap. This enables specifying the exact timings of each action and composing multiple actions in sequence or at overlapping intervals. To generate composite animations from a multi-track timeline, we propose a new test-time denoising method. This method can be integrated with any pre-trained motion diffusion model to synthesize realistic motions that accurately reflect the timeline. At every step of denoising, our method processes each timeline interval (text prompt) individually, subsequently aggregating the predictions with consideration for the specific body parts engaged in each action. Experimental comparisons and ablations validate that our method produces realistic motions that respect the semantics and timing of given text prompts.

AK

126,548 次观看 • 2 年前