Luma AI Unveils Ray3 Modify: Redefining Video Transformation

by THEFUTURE.TEAM

December 18, 2025

Innovative AI Model Transforms Video Editing

Luma AI, the a16z-backed leader in AI video and 3D modeling technology, has introduced its latest innovation, Ray3 Modify. This groundbreaking model allows creators to modify existing footage by using character reference images that faithfully preserve the original performance. By specifying start and end frames, users can seamlessly generate transitional footage, elevating the creative process.

Preserving Authentic Performance

Ray3 Modify addresses a critical challenge in visual production: maintaining the integrity of human performance amidst digital transformation. According to Luma AI, the model ensures that key elements—such as motion, timing, eye line, and emotional delivery—are retained, enabling creative studios to incorporate human actors within AI-modified scenarios. This capability is essential for studios aiming to produce consistent and high-quality brand or creative content.

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Expanding Creative Possibilities

Beyond preserving performance, Ray3 Modify empowers creators by facilitating detailed character transformations. Users may provide a reference image to transform an actor’s appearance, retaining crucial details like costumes, likeness, and identity across the shoot. Additionally, by offering start and end frames, directors can meticulously control transitions and character behavior, ensuring smooth continuity between scenes.

Strategic Industry Implications and Funding Momentum

In a statement, Amit Jain, co-founder and CEO of Luma AI, emphasized that the development of generative video models has historically been challenged by issues of control. “Generative video models are incredibly expressive but also hard to control. Today, we are excited to introduce Ray3 Modify that blends the real-world with the expressivity of AI while giving full control to creatives,” Jain explained. The model is now available via the company’s Dream Machine platform.

This release follows significant market developments, including Luma AI’s recent funding surge. A fresh $900 million funding round, led by Saudi Arabia’s Humain and witnessed by investors such as a16z, Amplify Partners, and Matrix Partners, underscores the high stakes in the AI-driven visual content arena. Furthermore, Luma AI’s strategic plans include building a 2GW AI cluster in Saudi Arabia, reinforcing its commitment to expanding technological capabilities and market reach.

Conclusion

With Ray3 Modify, Luma AI is setting a new benchmark in the integration of AI with video production. By offering unprecedented control over digital transformations without compromising on performance authenticity, the new model positions the company at the forefront of an industry undergoing rapid evolution.

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Meta Unveils Muse Wonder Models In A Bid For Frontier AI Leadership

by THEFUTURE.TEAM

October 13, 2026

Meta has introduced two new AI models, Muse Wonder and Muse Wonder Full, signalling a more ambitious move into frontier AI. The releases build on Muse Spark but target more demanding tasks, including coding, while placing greater emphasis on long-term memory and personalised assistance.

Larger Models Target More Demanding Tasks

Unlike Muse Spark, which was designed for low latency and lightweight use, Muse Wonder and Wonder Full are significantly larger and, according to Meta, more capable. The company reports stronger benchmark performance and improvements on demanding workloads, including coding.

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According to Meta’s model card, both models achieved benchmark results comparable to Fable 5.1 and GPT-6 Astra in the company’s internal testing. Meta described the release as “the next step on our scaling ladder and a major leap toward personal superintelligence” in its announcement.

Pricing Reflects Meta’s Frontier Ambitions

The models’ pricing places them in the premium AI segment. Through the Meta Model API, Muse Wonder will cost $4 per million input tokens and $20 per million output tokens, putting it in a similar pricing tier to GPT-6 Sol and Opus 5.5.

Muse Wonder Full, by contrast, will not be offered through the API. Meta says it will be exclusive to Muse Code subscribers, tying access to its most advanced model to a premium product. Both models will also power the Muse agent.

Long-Term Memory Takes Centre Stage

Meta is increasingly positioning its AI strategy around assistants that can support users over time rather than respond only to individual prompts. A central part of that approach is what the company calls “active memory”: the ability to retain, organise and synthesise information provided by users across extended periods.

Meta points to the MemoryBench benchmark, which evaluates systems designed for continual learning and memory construction. The company says Muse Wonder now outperforms Fable, Opus and Astra on this measure.

“We put a tremendous amount of effort into making sure your model can remember what’s important to you over days, weeks, months, and beyond,” research scientist Kavya Kopparapu said in a launch video.

New Models Follow Meta’s AI Reorganisation

The launch is also significant within Meta’s broader AI strategy. It is the company’s first frontier-model release since Scale AI founder Alexandr Wang reorganised its AI operations in August 2025. Meta has since introduced Muse Spark and the Muse agent, while making fewer high-profile model announcements than competitors such as OpenAI and Anthropic.

The new models were trained at Meta’s Prometheus data centre in central Ohio, which came online earlier in 2026, according to the company. The facility highlights the growing importance of infrastructure in the race to develop advanced AI systems, where performance depends not only on algorithms and data but also on computing capacity, data-centre investment and access to capital.

As competition intensifies, Muse Wonder and Wonder Full represent a broader bet on larger models, persistent memory and premium AI products. Their longer-term significance will depend on how their reported capabilities translate into real-world performance and whether users find enough value to pay for access.

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