Card Flip Animations

Hey Guys, it is the second decade of the second millennium and we are still kicking around the same 2D interface we got three decades ago. Sure, Apple debuted a few apps for OSX 10.7 that have a couple more 3D flourishes, and Microsoft has had that Flip 3D for a while. But c’mon, 2011 is right around the corner. That’s Twenty Eleven folks. Where is our 3D virtual reality? By now, we should be zipping around the Metaverse in super-sonic motorbikes. Granted, the capability of rendering complex 3D environments has been present for years. On the Web, there already are number of solutions – Flash, three.js in canvas, and eventually WebGL. And finally, we meager front-end developers have our own three-dimensional jewel: CSS 3D Transforms!




<span class="token tag"><span class="token punctuation"><</span>div <span class="token attr-name">class<span class="token punctuation">="</span>flip-container<span class="token punctuation">"</span></span> <span class="token attr-name">ontouchstart<span class="token punctuation">="</span>this.classList.toggle(<span class="token punctuation">'</span>hover<span class="token punctuation">'</span>);<span class="token punctuation">"</span>></span></span> <span class="token tag"><span class="token punctuation"><</span>div <span class="token attr-name">class<span class="token punctuation">="</span>flipper<span class="token punctuation">"</span>></span></span> <span class="token tag"><span class="token punctuation"><</span>div <span class="token attr-name">class<span class="token punctuation">="</span>front<span class="token punctuation">"</span>></span></span> <span class="token comment" spellcheck="true"><!-- front content --></span> <span class="token tag"><span class="token punctuation"></</span>div<span class="token punctuation">></span></span> <span class="token tag"><span class="token punctuation"><</span>div <span class="token attr-name">class<span class="token punctuation">="</span>back<span class="token punctuation">"</span>></span></span> <span class="token comment" spellcheck="true"><!-- back content --></span> <span class="token tag"><span class="token punctuation"></</span>div<span class="token punctuation">></span></span> <span class="token tag"><span class="token punctuation"></</span>div<span class="token punctuation">></span></span> <span class="token tag"><span class="token punctuation"></</span>div<span class="token punctuation">></span></span>

There are two content panes, “front” and “back”, as you would expect, but also two containing elements with very specific roles explained by their CSS. Also note the ontouchstart piece which allows the panes to swap on touch screens. Obviously you should break that code into a separate, unobtrusive JavaScript block if you wish.

/* entire container, keeps perspective */
.flip-container {
	perspective: 1000px;
}
	/* flip the pane when hovered */
	.flip-container:hover .flipper, .flip-container.hover .flipper {
		transform: rotateY(180deg);
	}

.flip-container, .front, .back {
	width: 320px;
	height: 480px;
}

/* flip speed goes here */
.flipper {
	transition: 0.6s;
	transform-style: preserve-3d;

	position: relative;
}

/* hide back of pane during swap */
.front, .back {
	backface-visibility: hidden;

	position: absolute;
	top: 0;
	left: 0;
}

/* front pane, placed above back */
.front {
	z-index: 2;
	/* for firefox 31 */
	transform: rotateY(0deg);
}

/* back, initially hidden pane */
.back {
	transform: rotateY(180deg);
}

Posted

in

by

Recent Post

  • 12 Essential SaaS Metrics to Track Business Growth

    In the dynamic landscape of Software as a Service (SaaS), the ability to leverage data effectively is paramount for long-term success. As SaaS businesses grow, tracking the right SaaS metrics becomes essential for understanding performance, optimizing strategies, and fostering sustainable growth. This comprehensive guide explores 12 essential SaaS metrics that every SaaS business should track […]

  • Bagging vs Boosting: Understanding the Key Differences in Ensemble Learning

    In modern machine learning, achieving accurate predictions is critical for various applications. Two powerful ensemble learning techniques that help enhance model performance are Bagging and Boosting. These methods aim to combine multiple weak learners to build a stronger, more accurate model. However, they differ significantly in their approaches. In this comprehensive guide, we will dive […]

  • What Is Synthetic Data? Benefits, Techniques & Applications in AI & ML

    In today’s data-driven era, information is the cornerstone of technological advancement and business innovation. However, real-world data often presents challenges—such as scarcity, sensitivity, and high costs—especially when it comes to specific or restricted datasets. Synthetic data offers a transformative solution, providing businesses and researchers with a way to generate realistic and usable data without the […]

  • Federated vs Centralized Learning: The Battle for Privacy, Efficiency, and Scalability in AI

    The ever-expanding field of Artificial Intelligence (AI) and Machine Learning (ML) relies heavily on data to train models. Traditionally, this data is centralized, aggregated, and processed in one location. However, with the emergence of privacy concerns, the need for decentralized systems has grown significantly. This is where Federated Learning (FL) steps in as a compelling […]

  • Federated Learning’s Growing Role in Natural Language Processing (NLP)

    Federated learning is gaining traction in one of the most exciting areas: Natural Language Processing (NLP). Predictive text models on your phone and virtual assistants like Google Assistant and Siri constantly learn from how you interact with them. Traditionally, your interactions (i.e., your text messages or voice commands) would need to be sent back to […]

  • What is Knowledge Distillation? Simplifying Complex Models for Faster Inference

    As AI models grow increasingly complex, deploying them in real-time applications becomes challenging due to their computational demands. Knowledge Distillation (KD) offers a solution by transferring knowledge from a large, complex model (the “teacher”) to a smaller, more efficient model (the “student”). This technique allows for significant reductions in model size and computational load without […]

Click to Copy