MiniMax 推广 H3 用于生成游戏角色动作精灵

What makes video generation particularly challenging is that videos are not just collections of independent images. They are complex temporal narratives where every frame must connect meaningfully to the next. Think about a simple scene like ”a cat walking across a garden”: the cat’s position, pose, and lighting must change smoothly from frame to frame while maintaining the cat’s distinctive features and the garden’s consistent appearance. Many existing methods treat temporal modeling as something to add on top of image generation, rather than designing it as a fundamental component from the ground up. This approach often fails spectacularly when dealing with complex scenes involving multiple moving objects, changing lighting conditions, or intricate interactions between scene elements. [...] Recent breakthroughs in text to image generation using diffusion models have been remarkable. We can now create stunning, photorealistic images from simple text prompts . However, extending this m

But look closer, and the same failures appear everywhere. Generated objects pass through surfaces instead of colliding. Characters reach for a cup but cannot grasp it. Physics breaks at the moment of interaction, and fine articulation collapses into pixelated blur. The models generate projections of a 3D world without understanding the world they are projecting. FIGURE 1 Structural failures in current SOTA models LTX 2.3 A glass of water falls from the table onto the ground. However, the man tries to stop it with a tennis racket. Veo 3.1 Two people pass a ball back and forth while a third person walks between them, briefly occluding the ball. Wan 2.6 Two dancers perform a lift where one partner throws the other into the air and catches them. FIGURE 1 [...] Kamo-1 currently conditions on character animation and camera in 3D. The environment, objects, and surfaces remain conditioned from the first frame onwards. The model produces a convincing 2D effect without maintaining a tru

## Motion Priors: What Real Movement Looks Like Temporal attention provides local consistency - it keeps neighboring frames looking similar. But consistency alone isn't enough. The model also needs to understand what realistic motion looks like. A consistent video where nothing moves isn't very useful. And a video with consistent frames but physically impossible motion looks just as wrong as one with flickering textures. This understanding comes from training data. Video generation models are trained on millions of real video clips - people talking, walking, smiling; wind blowing through trees; water flowing; cars driving. From this enormous corpus, the model learns motion priors: statistical patterns about how things typically move in the real world. [...] ## Motion Priors: What Real Movement Looks Like Temporal attention provides local consistency - it keeps neighboring frames looking similar. But consistency alone isn't enough. The model also needs to understand what realistic mo

It follows prompts very accurately and keeps the character's identity consistent throughout the video. One of MiniMax H3's biggest strengths

The workaround is to run faceswap over the results. The face swap models do a much better job at faces than the generic image generation models

Sota video generation models like our MiniMax H3 understand the movement of a walking character so well, while some people report that latest