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This is freaking awesome 🔥 Uthana just launched a new AI for 3D character animation. It allows users to upload any rigged character, prompt motions via text or video. Great for game dev and animators.

53,863 次观看 • 1 年前 •via X (Twitter)

12 条评论

Amira Zairi 的头像
Amira Zairi1 年前

Awesome 🔥 🤩

Moescape AI 的头像
Moescape AI1 年前

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

Aj 的头像
Aj1 年前

Gaming and VFX industry this year.

Farhan 的头像
Farhan1 年前

Name is quite desi haha

AshutoshShrivastava 的头像
AshutoshShrivastava1 年前

🤣🤣

Scott Stirling 的头像
Scott Stirling1 年前

@Scobleizer Yeah that’s wild

kvick 的头像
kvick1 年前

ah, tried it out, seems pretty bad ngl. Tried 4 different prompts. Still interested to see how it develops though

AshutoshShrivastava 的头像
AshutoshShrivastava1 年前

This doesn't look good .. I will run few test today if possible.

sanchay 的头像
sanchay1 年前

damn

AshutoshShrivastava 的头像
AshutoshShrivastava1 年前

Bhai ab game bano mast se..

Upesh🟡 的头像
Upesh🟡1 年前

Intresting

AshutoshShrivastava 的头像
AshutoshShrivastava1 年前

Yeah man.

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

126,595 次观看 • 2 年前