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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 просмотров • 1 год назад •via X (Twitter)

Комментарии: 10

Фото профиля ALCHEMIST AI 🔮
ALCHEMIST AI 🔮1 год назад

Built By: @3iside Download App:

Фото профиля Moescape AI
Moescape AI1 год назад

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

Фото профиля Syrate
Syrate1 год назад

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

Фото профиля Rex
Rex1 год назад

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

Фото профиля @JAX7
@JAX71 год назад

wen moon?

Фото профиля Ciph Xyz
Ciph Xyz1 год назад

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

Фото профиля Cody Apple 🪨
Cody Apple 🪨1 год назад

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

Фото профиля Vortecia
Vortecia1 год назад

Any plans for a UI/UX overhaul soon?

Фото профиля Shaikh Yasin
Shaikh Yasin1 год назад

I got liquideted worst coin scam

Фото профиля Z
Z1 год назад

It’s kinda insane how fast alch evolving $ALCH

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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.

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126,548 просмотров • 2 лет назад