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byebye expensive motion tracking equipment 👋 ai makes motion capturing so easy now! nvidia presented GENMO last week, a new model that can generate and estimate human motion from text, audio, video, and 3D keyframes

73,693 görüntüleme • 1 yıl önce •via X (Twitter)

10 Yorum

PowerBeatsVR profil fotoğrafı
PowerBeatsVR3 yıl önce

Get ready for a full-body VR workout that’s fun, fast, and intuitive — Play PowerBeatsVR (Now on Meta Quest) 🔥

Paliesk Debesį profil fotoğrafı
Paliesk Debesį1 yıl önce

Is it real time? Would love to use something like this for VR chat.

Adrian Werner profil fotoğrafı
Adrian Werner1 yıl önce

It's not going to replace expensive motion tracking for high end production because the fidelity is too low. But it is a cool stuff to have for indie studios. It's not anything new, plenty of such systems are already in use, for example inZOI has inhouse one.

NΞXUS STUDIO ⒶI profil fotoğrafı
NΞXUS STUDIO ⒶI1 yıl önce

Awesome, is it possible to generate a tracking shot of a car too?

Atiko 💎 profil fotoğrafı
Atiko 💎1 yıl önce

Wow

BLENDER SUSHI 🫶 X - 24/7 Blenderian profil fotoğrafı
BLENDER SUSHI 🫶 X - 24/7 Blenderian1 yıl önce

Fingers typing behind clothes :)

JSFILMZ profil fotoğrafı
JSFILMZ1 yıl önce

ai mocap been out for like 5 years

WaveSpeedAI profil fotoğrafı
WaveSpeedAI1 yıl önce

Cool!

Jorge profil fotoğrafı
Jorge1 yıl önce

some people might complain about "le ai is taking le work" but actually you still gotta know dem moves I've seen people doing mocaps at home and moving really weirdly, with like 3000€ equipment

rey profil fotoğrafı
rey1 yıl önce

@grok buddy, wt the hell is going on, I thought this wasn't suppose to come till 2030 ,r we in the singularity already 😂

Benzer Videolar

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 görüntüleme • 2 yıl önce