Fine-tuning GPT-3 to scale video creation
Fine-tuning GPT-3 to power and scale done-for-you video creation.
Invideo is leveraging GPT‑6 Astra to enhance its editing workflow, achieving more precise plans for edits and improving color correction and grading three times faster. The integration also enables the creation of 50 custom effects in a single day.
Fine-tuning GPT-3 to power and scale done-for-you video creation.
Higgsfield AI has released new video features powered by GPT‑6 Astra, enabling small businesses to create video ads more easily. The update also introduces fresh creative tools that accelerate the time it takes to bring new ideas to market.
Legora employed GPT‑6 Astra to review 41 documents in just minutes, successfully identifying all four planted errors. The use of the model also led to a nearly 40% improvement in performance within this financial‑review workflow.
Invideo AI uses OpenAI’s GPT-4. 1, gpt-image-1, and text-to-speech models to transform creative ideas into professional videos in minutes.
OpenAI introduces GPT‑6.1 Sol, a new model described as near‑Astra intelligence for coding, computer use, and professional work. It offers these capabilities at one‑fifth of Astra’s standard API input and output token prices, implying a more cost‑effective solution for developers and businesses.
Developers can now fine-tune GPT-4o with images and text to improve vision capabilities
The OpenAI Blog article titled "Cognition helps Devin test its own work with GPT‑6 Astra" discusses how GPT‑6 Astra enhances Devin’s capability to test software and demonstrate its functionality. This improvement aims to enable engineers to review less code and accelerate shipping of products.
OpenAI introduces GPT‑6 Astra, positioning it as the most intelligent and aligned model to date. The new model boasts state‑of‑the‑art capabilities in computer use, coding, cybersecurity, and science. It represents a significant step forward in AI performance and alignment.
arXiv:2604.18980v2 Announce Type: replace Abstract: Reducing the number of Gaussian-tile pairs is one of the most promising approaches to improve 3D Gaussian Splatting (3D-GS) rendering speed on GPUs...
arXiv:2609.40356v1 Announce Type: cross Abstract: Recent video generation is increasingly realistic and controllable, yet video editing remains less developed, particularly for precise local edits th...
arXiv:2607.26694v3 Announce Type: replace Abstract: We present Visko Orbis 1.0, a Live Model for real-time, interactive long video generation. Users can change the prompt at any moment during generat...
OmniEdit-Bench introduces a comprehensive benchmark for instruction-based video editing (IVE), addressing limitations of existing datasets by covering spatial, temporal, audio, and reference-based editing tasks and distinguishing explicit from implicit instructions. The evaluation framework assesses editing quality across accuracy, preservation, realism, and consistency, using human judgments and vision-language models, and incorporates an accuracy-aware penalty to ensure instruction fidelity. Experiments reveal that current IVE models perform poorly, highlighting the need for improved methods.