Paraform Studio
192 posts

Paraform Studio
@paraform
We are a creative studio using design, motion and technology to form new perspectives for brands, creating content that connects and brands that move
London, England انضم Ocak 2017
149 يتبع158 المتابعون

Cool add-on that allows you to use ZBrush style shortcuts, navigation and silhouette previews in Blender!
ModelingHappy@happy_modeling
Bbrush Zbrushと似たショートカットと左上に常にシルエット表示させてスカルプト作業出来るBlender用無料アドオン modelinghappy.com/archives/63156 #b3d #Blender3d
English
Paraform Studio أُعيد تغريده

The Karate Cat has completed its first full day—AND WE’RE JUST GETTING STARTED! 🚀🔥
Huge respect to the strongest dojo in crypto! 🏆💎 In just 24 hours, we’ve built a community of warriors ready to kick FUD and fight for greatness. The legend grows from here.
#TheKarateCat
CbvWbWgK34Qna5jQdpkiZ2jnUwrXT46bu9tnMSE4moon
English

@motionpunk1 We are using blender more and more, every time we go back to C4D it feels so slow in comparison.
English
Paraform Studio أُعيد تغريده

Playing with an early Tailwind CSS v4 alpha in a @vite_js project —
🚫 No `postcss.config.js file
🚫 No `tailwind.config.js` file
🚫 No configuring `content` globs
🚫 No `@tailwind` directives in your CSS
The future is clean ✨
Hoping to open-source this week for the bold 🤙🏻

English

New tutorial on how to use the Direct Link Update for
@MaxonVFX Cinema 4D and @UnrealEngine 5, which allows you to sync your projects in real-time and create stunning scenes and animations 👨🏿💻🤙🏿
Winbush@JonathanWinbush
Direct Link Update from @maxon to sync to @UnrealEngine 5 in just a click 👨🏿💻youtu.be/ezKCsIzQvxg?si…
English


@OpenAI new Sora text to video ai is incredible. No more hours of searching through stock libraries, just hyper realistic video from prompts.
English
Paraform Studio أُعيد تغريده

Diffusion World Model
paper page: huggingface.co/papers/2402.03…
introduce Diffusion World Model (DWM), a conditional diffusion model capable of predicting multistep future states and rewards concurrently. As opposed to traditional one-step dynamics models, DWM offers long-horizon predictions in a single forward pass, eliminating the need for recursive quires. We integrate DWM into model-based value estimation, where the short-term return is simulated by future trajectories sampled from DWM. In the context of offline reinforcement learning, DWM can be viewed as a conservative value regularization through generative modeling. Alternatively, it can be seen as a data source that enables offline Q-learning with synthetic data. Our experiments on the D4RL dataset confirm the robustness of DWM to long-horizon simulation. In terms of absolute performance, DWM significantly surpasses one-step dynamics models with a 44% performance gain, and achieves state-of-the-art performance.

English






