Paraform Studio

192 posts

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Paraform Studio

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 Se unió Ocak 2017
149 Siguiendo158 Seguidores
Paraform Studio retuiteado
TheKarateCat
TheKarateCat@The_Karate_Cat·
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
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Paraform Studio
Paraform Studio@paraform·
@motionpunk1 We are using blender more and more, every time we go back to C4D it feels so slow in comparison.
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motionpunk
motionpunk@motionpunk1·
Cinema4D has been in its most unreliable state since R18. Every day I open the same file and caching the same sim. And every time is different... The timeline/viewport refreshing is causing this most likely. These fundamental bugs exist for years but Maxon refuses to listen...
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Paraform Studio retuiteado
Adam Wathan
Adam Wathan@adamwathan·
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 🤙🏻
Adam Wathan tweet media
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Adam Wathan
Adam Wathan@adamwathan·
Little whirlwind tour of some theme customization workflows in Tailwind CSS v4 🌪️
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Paraform Studio
Paraform Studio@paraform·
@OpenAI new Sora text to video ai is incredible. No more hours of searching through stock libraries, just hyper realistic video from prompts.
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Paraform Studio
Paraform Studio@paraform·
We’ve been so busy in 2023 that we forgot to post! Here is some work bringing to life the Art of Italian Dressing for Mazetti and our friends Boundless. #redshift #c4d
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Paraform Studio retuiteado
AK
AK@_akhaliq·
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.
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