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Character-2 is here! A new generational leap for audio to video foundation models, and our next step towards a full-stack character model capable of intelligence, emotion, and more. Key highlights: * Widescreen and vertical support: removing limitations on aspect ratio. * A massive quality boost: improved clarity, more expressive,... show more

Hedra

54,355 subscribers

438,389 Aufrufe • vor 1 Jahr •via X (Twitter)

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Profilbild von Hedra

Hedravor 1 Jahr

Available now at

Profilbild von Michael Lingelbach

Michael Lingelbachvor 1 Jahr

We are hiring

Profilbild von andrew chen

andrew chenvor 1 Jahr

Go @mjlbach and team!!

Profilbild von nikshep

nikshepvor 1 Jahr

when public API 👀

Profilbild von Jon Lai

Jon Laivor 1 Jahr

so excited for 16:9 support! 🔥

Profilbild von Martin Gent

Martin Gentvor 1 Jahr

Perfect timing for my next project! Thanks @hedra_labs

Profilbild von Bilawal Sidhu

Bilawal Sidhuvor 1 Jahr

Let’s go! Stoked for 9:16 support

Profilbild von LEV3LUP

LEV3LUPvor 1 Jahr

Amazing news! So excited for this!

Profilbild von Bobcat

Bobcatvor 1 Jahr

Damn I could have used this 48 hours ago, now I've hit my monthly quota...

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Today we are releasing Character-1.5 and Stylize. Available now at This update introduces stylization, allowing you to transform into a character while maintaining your identity. Character-1.5 brings sharper visual output, more lifelike animations with natural blinking and head movements, and improved handling of uploaded images. It's a major leap forward for our foundation model before we release Character-2.

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Introducing GPT-4o, our new model which can reason across text, audio, and video in real time. It's extremely versatile, fun to play with, and is a step towards a much more natural form of human-computer interaction (and even human-computer-computer interaction):

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[SIGGRAPH Asia '24 (TOG)] Representing Long Volumetric Video with Temporal Gaussian Hierarchy Contributions: • We introduce a novel, efficient, and expressive Temporal Gaussian Hierarchy representation for long volumetric video. To our knowledge, our method is the first approach capable of handling minutes of volumetric video data. • We propose a Compact Appearance Model and a new rasterization implementation to facilitate real-time, high-quality dynamic view synthesis while maintaining a compact size. • We propose a system to efficiently model long volumetric videos for the first time and demonstrate state-of-the-art dynamic view synthesis quality on the Neural3DV [Li et al. 2022], ENeRF-Outdoor [Lin et al. 2022], and MobileStage [Xu et al. 2024b] datasets, while also achieving the best rendering speed with reduced training cost and memory usage.

[SIGGRAPH Asia '24 (TOG)] Representing Long Volumetric Video with Temporal Gaussian Hierarchy Contributions: • We introduce a novel, efficient, and expressive Temporal Gaussian Hierarchy representation for long volumetric video. To our knowledge, our method is the first approach capable of handling minutes of volumetric video data. • We propose a Compact Appearance Model and a new rasterization implementation to facilitate real-time, high-quality dynamic view synthesis while maintaining a compact size. • We propose a system to efficiently model long volumetric videos for the first time and demonstrate state-of-the-art dynamic view synthesis quality on the Neural3DV [Li et al. 2022], ENeRF-Outdoor [Lin et al. 2022], and MobileStage [Xu et al. 2024b] datasets, while also achieving the best rendering speed with reduced training cost and memory usage.

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Researchers at Meta recently shared MAGNeT, a single non-autoregressive transformer model for text-to-music & text-to-sound generation capable of generating audio on-par with the quality of SOTA models — at 7x the speed. MAGNeT is open source as part of AudioCraft. Hear audio samples and get more details on this work ➡️

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Today we're announcing the open-source release of HunyuanVideo-Foley, our new end-to-end Text-Video-to-Audio (TV2A) framework for generating high-fidelity audio.🚀 This tool empowers creators in video production, filmmaking, and game development to generate professional-grade audio that precisely aligns with visual dynamics and semantic context, addressing key challenges in V2A generation.🔊 Key Innovations: 🔹Exceptional Generalization: Trained on a massive 100k-hour multimodal dataset, the model generates contextually-aware soundscapes for a wide range of scenes, from natural landscapes to animated shorts. 🔹Balanced Multimodal Response: Our innovative multimodal diffusion transformer (MMDiT) architecture ensures the model balances video and text cues, generating rich, layered sound effects that capture every detail—from the main subject to subtle background elements. 🔹High-Fidelity Audio: Using a Representation Alignment (REPA) loss function and a powerful Audio VAE, we've improved generation stability and producing professional-grade audio, free of noise and inconsistencies. HunyuanVideo-Foley achieves SOTA on multiple benchmarks, surpassing all open-source models in audio quality, visual-semantic alignment, and temporal alignment. 👉Try it now: 🌐Project Page: 🔗Code: 📄Technical Report: 🤗Hugging Face:

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