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Explore AI generated designs, images, art and prompts by top community artists and designers.
Close-up cinematic portrait of a woman wearing a sleek futuristic transparent headset , techcore aesthetic , soft neon lighting reflecting across her face , highly detailed skin texture , shallow depth of field. Transparent holographic HUD elements floating around her—glowing interfaces with red number 9000 , data streams , and virtual landscapes projected in mid-air. The holograms depict romantic futuristic imagery: soft digital sunsets couple kissing , couples walking hand in hand glowing city skylines , red glowing number 9000 , intertwined light forms , and dreamlike virtual environments. Color palette of violet , cyan , and warm pink tones , blending technology with intimacy. High contrast lighting , subtle lens flare , ultra-realistic , 8k detail , cyberpunk-inspired but elegant and emotional , immersive atmosphere. , head and shoulders portrait , 8k resolution concept art portrait by Greg Rutkowski , Artgerm , WLOP , Alphonse Mucha dynamic lighting hyperdetailed intricately detailed Splash art trending on Artstation triadic colors Unreal Engine 5 volumetric lighting ,
Vision-Language-Action (VLA) models have emerged as a promising paradigm for robot learning , but their representations are still largely inherited from static image-text pretraining , leaving physical dynamics to be learned from comparatively limited action data. Generative video models , by contrast , encode rich spatiotemporal structure and implicit physics , making them a compelling foundation for robotic manipulation. But their potentials are not fully explored in the literature. To bridge the gap , we introduce DiT4DiT , an end-to-end Video-Action Model that couples a video Diffusion Transformer with an action Diffusion Transformer in a unified cascaded framework. Instead of relying on reconstructed future frames , DiT4DiT extracts intermediate denoising features from the video generation process and uses them as temporally grounded conditions for action prediction. We further propose a dual flow-matching objective with decoupled timesteps and noise scales for video prediction , hidden-state extraction , and action inference , enabling coherent joint training of both modules. ,
